Research Session Coordination University of Nebraska-Lincoln Research Conference Coordination Cornell University

American Association for Agricultural Education NORTH CENTRAL REGION CONFERENCE

PROCEEDINGS Monday, September 22, 2008

Mann Library Ithaca, New York

1

Table of Contents

Post Secondary Teaching

Leadership

Secondary Education

Session

Title

Author(s)

An Exploration of the Relationship between the Motivational Profile of Secondary Students Enrolled in a Comprehensive Ag Program and Academic Achievement

Anderson II, J. C., & Torres, R. M.

4

West Virginia Agricultural Education Teachers Perceptions on Involving Students with Exceptionalities in Agricultural Classrooms and Laboratories

Boone, D. A., Watts, A., Boone, Jr., H. N., & Gartin, S. A.

22

Factors Influencing Participant Motivation and Engagement in the XX Youth Farm Stand Project

Rivera-Caudill, J. E., & Brander, A. A.

37

Nebraska Elementary Teachers’ Understanding of Agricultural Concepts: A Case Study in Educational Service Unit #2

Turnbull, S. M., & Miller, W. W.

51

Leadership Courses Required in Agricultural Teacher Education Programs

Simonsen, J. C., & Birkenholz, R. J.

66

Internationalizing Leadership Development: Important Components within Educational International Leadership Experiences

Ricketts, K. G., & Morgan, C.

81

Examining Undergraduate Student Involvement in Collegiate Student Organizations in Colleges of Agriculture

von Stein, M. F., & Ball, A. L.

95

Pioneers in an Emerging Field: Who Were the Early Agricultural Educators?

Foor, R. M., & Connors, J.

111

Creative and Effective Teaching Behaviors of University Instructors

Aschenbrener, M. S., Terry, R., & Torres, R. M.

125

Faculty Knowledge and Perceptions of the Scholarship of Teaching and Learning

Maxwell, L. D., Ball, A. L., & Irani, T.

139

The Effect of an Integrated Course Cluster Learning Community on the Oral and Written Communication Skills and Technical Content Knowledge of Upper-Level College of Agriculture Students

Barnett, C., Miller, G., Polito, T. A., & Gibson, L.

152

Major Comparison of Cognitive Potential: Are Agriculture Students Different?

Rhoades, E. B., Ricketts, J. C., & Friedel, C. R.

165

2

Page

Extended Education and Outreach

Mentoring and Advising

Secondary Agricultural Education

Session

Title

Author(s)

Page

Changes in Teacher Self-Efficacy and Perceptions of Preparation of Agricultural Education Teacher Candidates

Wolf, K. J., Foster, D. D., & Birkenholz, R. J.

180

Are They Satisfied? Using Agricultural Education Graduates’ Learning Style to Assess Their Job Satisfaction

Robinson, J. S., Kitchel, T., & Garton, B. L.

192

Predictors of Job Stress Among Agriculture Education Teachers

Torres, R. M., Lambert, M. D., & Lawver, R. G.

204

Job Stress among Agriculture Teachers: Highs and Lows

Torres, R. M., Lawver, R. G., & Lambert, M. D.

217

Does Mind Matter? Mind Styles and Satisfaction Among Mentor/Protégé Pairs in Missouri

Ulmer, J. D., Lambert, M. D., & Smith, A. R.

229

What Do Agricultural Education Students Want? Meeting Our Students’ Needs Through Faculty Advising

Smith, A. R., & Garton, B. L.

240

Undergraduate Mentoring: What Do Students Think?

Retallick, M. S., & Pate, M. L.

255

Teachers as Planners of Professional Development Conferences: Utilizing Professional Expertise to Select Conference Components

Moore, D. M., & Camp, W. G.

269

Participation of Agricultural Education Teachers in the Planning of Professional Development Conferences: Influence of Teachers on the Conference Format

Moore, D. M., & Camp, W. G.

287

Evaluation of Information Transfer between Extension Agents and Dairy Producers in Pennsylvania

Nelson, C. B., Boone, D. A., Boone, Jr., H. N., & Woloshuk, J. M.

302

3

AN EXPLORATION OF THE RELATIONSHIP BETWEEN THE MOTIVATIONAL PROFILE OF SECONDARY STUDENTS ENROLLED IN A COMPREHENSIVE AG PROGRAM AND ACADEMIC ACHIEVEMENT James C. Anderson II, University of Illinois Robert M. Torres, University of Missouri Abstract This study examined the personal factors that may affect the self-determination of students who elected to enroll in a comprehensive agriculture program. A group of 114 freshmen were randomly selected through a computerized lottery from approximately 1500 applicants from various public and private grade schools throughout the city to attend the Chicago High School for Agricultural Sciences. The personal factors, also known as the motivational profile, consisted of the student’s academic aptitude, the type of motivation to attend school, influences in the decision to attend the high school, satisfaction with the decision to attend, and perceived effort during academic tasks related to agriculture. The results show that the sample reported having a choice in the decision to attend the school. This perceived autonomy may have contributed to the effort they put into academic tasks and thus improved academic achievement. In addition, significant relationships were found between gender and the motivational profile, between the factors influencing autonomy support and outcomes of self-determination, and among factors in the motivational profile and academic achievement. The indented use of the motivational profile is to help understand the relationship among personal, environmental and behavior factors in students in order to develop interventions that target student engagement and academic achievement. Introduction Engagement and motivation towards learning has been related to positive adaptation in academic environments; including the reduction of dropout rates and increase in levels of student success (Blank, 1997; Kushman, 2000; Woods). However, maintaining students’ interest in school and motivating them to succeed are challenges that even the most experienced of teachers face. Studies have shown that student engagement in school drops periodically as students get older. Lack of interest in schoolwork, homework and school related activities come into consideration around the time students reach middle school (Anderman & Midgley, 1998; Sullivan, Tobias, & McDonough, 2006; Lumsden, 1994). Furthermore, motivation to complete academic tasks is affected by various unique factors (Bandura, 1986). One factor that affects motivation is an individual’s environment; the influence of teachers, parents, siblings, classmates, friends, and the existence of other activities that compete for the attention and time of the student. Another factor is personal; the individual’s aptitude, self-efficacy, self-regulatory processes, and other abilities (Bandura). There are important individual differences among learners both in motivation to perform academic tasks and preferences about when, where, how, and with whom they prefer to perform (Hong & Milgram, 2000). A number of studies have shown that an individual’s learning techniques and the conditions under which academic tasks are done increase compliance with these tasks and raises academic progress (Hong, Tomoff,

4

Wozniak, Carter, & Topham, 2000). The question remains, what can an educator do to engage students who lack motivation to persist in educational endeavors? Dewey (1938) highlighted two strong and opposing viewpoints about what motivates a learner and how to structure education in accord with each viewpoint. First, there is the theory that motivation for learning comes from outside the learner. They must receive structures, rewards, and incentives in order to be successful in school. This viewpoint emphasizes the teacher providing extrinsic controls to motivate students. The other theory assumes that motivation is already present and can be catalyzed or facilitated in the context of school. This is accomplished by the teacher and parents providing encouragement or nurturing the students’ educational interests. The initiation, direction, intensity and persistence of such human behavior is called motivation (Green, 1994). Since its inception, motivation has been studied from several perspectives (e.g. deCharms, 1976; Deci, 1975; Deci & Ryan, 2000; Evelein, Korthagen, & Brekelmans, 2008; McClelland, 1987; Rutter, Smith, & Hall, 2002; Stanford & Couch, 1985; Vallerand & Bissonette, 1992). Some studies have focused on the delineation of types of motivation while others have focused on understanding the regulatory processes of motivation. The most widely used perspective on the regulatory processes in recent years suggests that behavior can be seen as intrinsically and extrinsically motivated (de Charms, 1968; Deci, 1971, 1975; Deci & Ryan, 2000). Intrinsic motivation refers to behaviors that an individual engages in for one’s own pleasure (Deci, 1971). The individual voluntarily performs an act in the absence of material rewards or constraints. They are satisfied just because they were able to perform the task. Conversely, extrinsically motivated behaviors are those that an individual engages in because the behaviors are a means to an end and not because of the internal satisfaction derived from the tasks (Deci, 1975; Kruglanski, 1978). Originally, it was believed that extrinsic motivation referred to behaviors an individual engaged in due to a lack of self-determination and therefore could only be prompted by external events (Vallerand & Bissonette). However, researchers have proposed that different types of extrinsic motivation exist (Deci & Ryan, 1985, 1987, 2000; Ryan & Connell, 1989; Vallerand & Bissonette). In their self-determination theory (SDT), Deci and Ryan (1985) introduced a subtheory, the organismic integration theory (OIT), to detail the different forms of extrinsic motivation and the contextual factors that either promote or hinder internalization and integration. The four types of extrinsic motivation are (a) external, (b) introjected, (c) indentified, and (d) integrated regulation (Vallerand & Bissonette, 1992). External regulation occurs when the behavior is regulated with outside inducements, typically with rewards or constraints. Introjected regulation occurs when behavior is internally regulated and the individual is self-imposing rewards or constraints. For example, a student might volunteer to answer a question but is only doing it because no one else will. Identified regulation occurs when a behavior is valued by the individual and is perceived as self-chosen. For example, a student decides to take advance placement courses because it will boost his grade point average. Finally, integrated regulation occurs when the behavior is performed because it fits within the individual’s self concept. For example, a student turns in all of her homework and studies for every exam instead of participating in leisure activities because she values her education and have integrated the behaviors needed to be successful in school into other facets of her life.

5

In addition, a third construct, amotivation, was suggested by Deci and Ryan (1985) in order to fully understand all facets of human behavior. Amotivation, occurs when an individual perceives a lack of contingency between their behavior and outcomes. The individual perceives no rewards or constraints by participating in the task. In this event, the individual will eventually cease participation. Amotivation occurs because the individual cannot identify a sense of purpose and has no expectation for reward or control over changing the course of events. It is likened to learned helplessness since the individual experiences feelings of incompetence and uncontrollability (Abrahamson, Seligman, & Teasdale, 1978; Vallerand & Bissonette, 1992). The starting point for SDT assumes that humans are active, growth-oriented organisms who are naturally inclined toward integration of their psychological elements into a unified sense of self and integration of themselves into large social structures (Ryan & Powelson, 1991). Therefore, the adaptive nature of humans incline them to engage in interesting activities, to stretch their capacities, to pursue connectedness in social groups, and to integrate psychological and interpersonal experiences into a relative unity. Simply stated, humans are motivated by an innate desire to satisfy the need for autonomy, competence, and relatedness (Ryan & Powelson). The term autonomy refers to “self-rule.” It describes an individual’s ability to regulate one’s behavior through governing the initiation and direction of actions. The term competence refers to the sense of accomplishment and effectiveness towards exercising one’s capabilities under challenging conditions. Individuals have an innate need to stretch their skills and schemata just beyond one’s current level of functioning. Finally, the term relatedness refers to the emotional and personal bonds between individuals. It reflects the human need for contact, support, and to commune with others. However, it does not just refer to a connection, it is also refers to the experience of developing well-being and cohesion with all individuals involved (Ryan & Powelson). The more an individual perceives a course of action will satisfy these needs the more self-determined that individual will become leading to more internally regulated and persistent behaviors (Deci & Ryan, 1991; Ryan & Powelson, 1991). According to the SDT, individuals are inherently motivated to integrate the self-regulation of extrinsically motivated activities that are useful for effective functioning in society but are not inherently interesting (Deci, Eghrari, Patrick & Leone, 1994). This is what has been termed internalization. Internalization concerns all regulations which were originally elicited through extrinsic incentives but have been transformed into regulations by self (Ryan, 1993). The degrees of reasons on the self-determination continuum is viewed as a reflection of the internalization process where the individual moves from the less self-determined forms of regulation (i.e., amotivated, external and introjected) to more self-determined forms (i.e., identified and intrinsic) (Pelletier, Fortier, Vallerand, & Bri`ere, 2001). Theorists purport that in order for students to utilize more self-determined regulation, the educational contexts must support an individual’s autonomy whereas contexts that are viewed as controlling are hypothesized to undermine selfdetermined motivation (Pelletier, et al.). The present public educational system requires that most students be assigned to a specific school based on residence and taught a prescribed curriculum. This format may be perceived by students as controlling and therefore students may display less self-determined forms of regulation. A major consequence of less self-determination would be disengagement from school and academic activities (Ryan & Powelson, 1991). Therefore it is proposed that allowing

6

students to have more decision-making power in academic decisions may create a sense of autonomy leading to increased engagement and the potential for higher levels of academic success. This study seeks to explore if the ability for students to elect into a comprehensive agricultural program provides the perceived autonomy that yields to increased engagement and subsequently academic achievement. Conceptual Framework The conceptual framework for this study borrows from Bandura’s (1986) social cognitive theory (SCT). Bandura’s social cognitive theory postulates that motivational processes influence both learning and performance of cognitive skills, social skills, motor skills, strategies, and behaviors (Pintrich & Schunk, 2002). He used self-efficacy as a key variable and integrated the motivational process with self-regulation (Bandura, 1986, 1989). Zimmerman (1998) described self-regulation in a social cognitive context as a cyclical process that is comprised of three phases: the forethought phase; the performance (volitional) control phase; and the self-reflection phase. Simply stated, with each learning task, students analyze how the task relates to their selfimage, decide on a path of action, and reflects on the internal and external factors that influenced the outcome. This within person interaction is noted with the small loop emanating from the personal factors in Figure 1. As students go through this cyclical process of self-regulation due to changes in personal, behavioral, and environmental factors, their strategies, cognitions, affects, and behaviors for learning will change as well (Pintrich & Schunk). However, instead of selfefficacy as the key variable for motivating the student, this framework used self-determination from an organismic perspective to explain personal factors of motivation. The adaptation is due to the fact that self-efficacy focused specifically on the extent to which people believed they were capable of engaging in behaviors that would lead to desired outcomes (Deci & Ryan, 2000). This belief of self-capability was formed by past extrinsic rewards or consequences and is driven by the desire to either obtain more rewards or avoid consequences. Unfortunately, this assertion did not take into account the complexity of motivation by addressing intrinsic motivation and amotivation (Deci & Ryan). Although selfdetermination theory and SCT have some similarities in that both have a self-regulatory component for motivation as well as address the effects of the environment on student behavior, they are two distinct theories with inherent differences and should be treated as such. It is for this reason that the organismic social cognitive perspective (OSCP) was developed to respect the inherent differences but address the effects of educational interventions on student motivation and engagement from a more holistic perspective (see Figure 1). Similar to the triadic reciprocality model for SCT, the OSCP model demonstrates the interaction between personal, environmental, and behavioral factors which influence students’ interests, engagement, and volition to learn. The term, motivational profile, was used to identify the personal factors associated with self-determination as either influences or outcomes. These factors are related to the satisfaction of the three basic psychological needs (i.e. autonomy, relatedness & competence). Autonomy and relatedness were measured using influences in the decision to attend the agricultural high school. According to Esters and Bowen (2004), parental influence is a factor in the decision for students to enroll in an urban agricultural education program. If this finding holds true for this sample, it is important to explore the impact parental

7

influence has on perceived autonomy support (i.e. autonomy and relatedness). Competence was measured using academic aptitude (7th grade reading T-score). In addition, outcomes of selfdetermination were measured using personal factors (i.e. types of motivation to attend school, satisfaction with decision to attend the agricultural high school, and perceived effort on academic tasks related to agriculture) and behavioral factors (i.e. academic achievement). Although types of motivation can be used as an influence of behavioral factors, it was only used as a personal outcome for the purpose of this study. Furthermore, environmental factors were beyond the scope of this study and were not addressed.

Academic Achievement

Behavioral

Motivational Profile

Personal

Environmental

Figure 1. Conceptual Model for the Organismic Social Cognitive Perspective Motivation is important to look at when discussing student academic achievement because research shows a relationship between motivation and students persistence in school (e.g. Ames, 1990; Rader, 2005; Reeve & Jang, 2006; Wolfe, 1996). A study published by Vallerand and Bissonnette (1992) purported that individuals who persisted in a course had reported being more intrinsically motivated, more identified and integrated, and less amotivated toward academic activities than students who dropped the course. They also revealed that females were more intrinsically motivated, integrated, and identified and less externally regulated and amotivated than males. These results may give claim to the hypothesis that individuals who are more selfdetermined will be more engaged in school as well as possess the adaptive mechanisms that yield greater academic achievement. Purpose & Research Objectives Using the term motivational profile, this study sought to describe the personal factors associated with self-determination. These factors can be described as events that occur in the cognitive, affective, and psychomotor domains. In order to understand how they influence an individual to act, motivation must first be defined and described. Thus, the purpose of this study was to examine the personal factors that may affect the self-determination of students who have elected to enroll in a comprehensive agricultural education program. By understanding the outcomes of these personal factors, the motivational profile can be used to help explain the 8

relationship among personal, environmental and behavior factors in students. Furthermore, interventions can be developed from this understanding that target student engagement and academic achievement. This exploratory study was guided by the following research objectives. 1. Describe students on gender, motivational profile (academic aptitude as measured by the Illinois Standardized Achievement Test (ISAT) in reading, type of motivation to attend school, influences in the decision to attend the agricultural high school, satisfaction with the decision to attend, and perceived effort during academic tasks related to agriculture), and academic achievement (first semester GPA). 2. Determine the relationships between gender and motivational profile (academic aptitude as measured by the ISAT in reading, type of motivation to attend school, influences in the decision to attend the agricultural high school, satisfaction with the decision to attend, and perceived effort during academic tasks related to agriculture). 3. Determine the relationships between factors influencing perceived autonomy (influences in the decision to attend the agricultural high school) and the outcomes of self-determination (type of motivation to attend school, satisfaction with decision to attend the school, perceived effort during academic tasks related to agriculture, and academic achievement). Methods and Procedures This study was descriptive-correlational in design. The population was freshmen students from Chicago, IL who were enrolled in the public school system. A computerized lottery was used to select 114 freshmen students from approximately 1500 applicants from various public and private grade schools throughout the city to attend the Chicago High School for Agricultural Sciences (CHSAS). Most agriculture programs are in schools that serve a small community. Due to resource limitations, CHSAS was selected because it served the entire metropolitan area, lending to more diversity in the sample and a better representation of students in Chicago. Students’ motivational profile was measured using an adapted version of the Academic Motivation Scale (AMS) – High School Version (Vallerand, Pelletier, Blais, Brière, Senécal, & Vallières, 1992). The scale measured intrinsic motivation, three forms of extrinisic motivation, amotivation, rate in which specified individuals influenced the student’s decision to attend the magnet school, and degree of satisfaction with that decision at the beginning of the school year and three months later (1 = Does not correspond at all, 2-3 = Corresponds a little, 4 = Corresponds moderately, 5-6 = Corresponds a lot, and 7 = Corresponds exactly). Students’ gender and 7th grade reading aptitude stanine were reported by the instructor. Stanines were then converted into T-scores by the investigator. First semester grade point average (GPA) was obtained from official records provided by the school. Vallerand et al. (1989; 1992; 1993) established validity of the AMS using confirmatory factor analysis to correlate each subscale among themselves and the tenets of Deci & Ryan’s (1985) motivational theory. These studies found that instrinsic motivation and amotivation were negatively correlated (r = -.82), which is predicted by self-determination theory. In addition, a panel of experts consisting of an educational psychologist, a methodologist, and three content experts reviewed the profile for face and content validity. Using a national sample of high school students, Cronbach’s alpha

9

coefficients for the subscales ranged from .58 to .84 (n = 1,062). Test-retest reliability displayed temporal stability with a mean correlation value of .79 over a one-month period. Test/retest was conducted on a pilot group (n = 28) to confirm reliability of the supplemental questions with a percent agreement of 82% or better. Parental, participant, and administrative consent was received prior to commencement of the study. All participants were invited to a general meeting room in the school to complete the data collection instrument. Each student was asked to provide their student identification number on the form. The data from the AMS was matched to each student’s academic aptitude score, semester GPA and gender by their student identification number. Data were analyzed in SPSS using descriptive statistics for research objective one, point-biserial correlations for research objectives two, and Pearson Product Moment correlations for research objective three. The alpha level was set at a .05 a priori. In addition, Davis’ convention (1971) was used to describe the magnitude of the correlations. Findings Research objective one sought to describe subjects on gender, motivational profile (academic aptitude as measured by the state reading assessment score, type of motivation to attend school, influences in the decision to attend the agricultural high school, satisfaction with that decision, and perceived effort during academic tasks related to agriculture), and academic achievement as measured by the first semester GPA. Of the 114 participants in this study, the majority were female (56%) and were categorized as meeting or above reading standards based on the state-wide standardized assessment scores. The state’s indication for meeting the reading standard is a T-score ranging from 50-56. The participants of this study T-scores ranged from 40 to 70 (M = 53.9, SD = 5.3). In terms of the participant’s type of motivation to attend school, the mean scores ranged from 4.6 to 6.2 for the intrinsic and extrinsic subscales and was 2.5 for the amotivation subscale (see Table 1). First semester GPAs ranged from 0.27 to 4.0 on a 4-point scale. The mean GPA for the sample was 2.23 (SD = 1.01). Table 1 Academic Motivation to Attend School (n = 114) Type of Motivation Intrinsic Motivation

Mean 4.6

SD 0.9

Identified Regulation

5.8

1.0

Introjected Regulation

6.2

0.9

External Regulation

5.7

1.2

Amotivation 2.5 1.5 Note. The ratings are as follows: 1 = Does not correspond at all, 2-3 = Corresponds a little, 4 = Corresponds moderately, 5-6 = Corresponds a lot, and 7 = Corresponds exactly.

10

Table 2 summarizes the influences on students’ decision to attend the agricultural high school, satisfaction with that decision, and perceived effort on academic tasks related to agriculture. The participants reported self (M = 4.9, SD = 1.9) as the strongest influence in the decision to attend the school followed by family decision (M = 4.2, SD = 2.1). The participants reported mothers as having a slight influence (M = 3.6, SD = 2.2) and fathers as having little influence (M = 3.0, SD = 2.2) on the decision to attend. The participants also reported that prior to the first day of class, as well as three months later, that they were moderately satisfied with the decision to go to the comprehensive agricultural high school with a mean score of 4.7. In addition, participants reported putting a high amount of effort (M = 5.4, SD = 1.3) into academic tasks related to agriculture. Table 2 Factors Influencing a Student’s Motivational Profile (n = 114) Influence Self-Selected to Attend

Mean 4.9

SD 1.8

Median 5.0

Mode 7

Family Decision to Attend

4.2

2.1

4.0

4

Mother’s Decision to Attend

3.6

2.2

4.0

1

Father’s Decision to Attend

3.0

2.2

3.0

1

Satisfaction Before School Began

4.8

2.1

5.0

7

Satisfaction Three Months Later

3.3

2.3

3.0

1

Perceived Effort 5.4 1.3 5.5 5.5 Note. The ratings are as follows: 1 = Does not correspond at all, 2-3 = Corresponds a little, 4 = Corresponds moderately, 5-6 = Corresponds a lot, and 7 = Corresponds exactly. Research objective two sought to determine the relationships between gender and the motivational profile (academic aptitude as measured by the state reading assessment, type of motivation to attend school, influences in the decision to attend the high school, satisfaction with the decision to attend, and perceived effort during academic tasks related to agriculture). There were three significant relationships between gender and the motivational profile (see Table 3). There were low and positive relationships between gender and introjected regulation (rpb = .24, p < .05) and external regulation (rpb= .22, p < .05). In addition, there was a low and negative relationship between gender and amotivation (rpb = -.28, p < .05).

11

Table 3 Point-Biserial Correlation between Gender and Motivational Profile (n = 114) Motivational Profile Academic Aptitude

Gender .09

Intrinsic Motivation

-.11

Identified Regulation

.17

Introjected Regulation

.24*

External Regulation

.22*

Amotivation

-.28*

Self-Selected to Attend

-.04

Family Decision to Attend

-.12

Mother’s Decision to Attend

.33

Father’s Decision to Attend

-.15

Satisfaction Before School Began

-.06

Satisfaction Three Months Later

.13

Perceived Effort

.10

Academic Achievement Note: 0 = Male & 1 = Female, *p < .05

.06

Research objective three sought to determine the relationships between factors influencing perceived autonomy (influences in the decision to attend the agricultural high school) and the outcomes of self-determination (type of motivation to attend school, satisfaction with decision to attend the school, perceived effort during academic tasks related to agriculture, and academic achievement). There were significant relationships between the factors influencing autonomy support and outcomes of self-determination (see Table 4). Self-selecting to attend the high school and intrinsic motivation (r = .39, p < .05) had a low and positive relationship and self-selection and introjected regulation (r = .26, p < .05) had a moderate and positive relationship. Also existing were a moderate and positive relationship between self-selection and satisfaction before school began (r = .45, p < .05) and a low and positive relationship between self-selection and satisfaction after three months (r = .29, p < .05).

12

There was a low and positive relationship between family decision and external regulation (r = .21, p < .05) and a moderate and positive relationship between family decision and intrinsic motivation (r = .31, p < .05). Family decision had a moderate and positive relationship (r = .38, p < .05) and mother’s decision had a low and negative relationship (r = -.22, p < .05) with satisfaction with the decision to attend the high school before school began. Amotivation had moderate and positive relationships with mother’s choice (r = .35, p < .05) as well as father’s choice (r = .42, p < .05) to attend the high school. Finally, there was a low and negative relationship (r = -.24, p < .05) between father’s choice and perceived effort on academic tasks related to agriculture. There was no observable relationship between academic achievement and factors influencing perceived autonomy. Table 4 Pearson Product-Moment Correlation between Factors Influencing Perceived Autonomy and Outcomes of Self-Determination (n = 114) Outcome Intrinsic Motivation

Self .39*

Family .31*

Mother -.13

Father ..03

Identified Regulation

.11

.14

-.03

-.08

Introjected Regulation

.26*

.17

.02

.00

External Regulation

.05

.21*

.07

.00

Amotivation

.00

.01

.35*

.42*

Satisfaction Before School Began

.45*

.38*

-.22*

-.08

Satisfaction Three Months Later

.29*

-.04

-.17

-.10

Perceived Effort

.06

.07

-.11

-.24*

Academic Achievement *p < .05

.00

.07

-.03

.01

There were significant relationships among the influences in the motivation to attend school, the satisfaction with the decision to attend the agricultural high school, the perceived effort on academic tasks related to agriculture, and academic achievement (see Table 5). There were low and positive relationships between intrinsic motivation and satisfaction before school began (r = .20, p < .05), three months later (r = .23, p < .05), and academic achievement (r = .20, p < .05). There was a moderate and positive relationship between intrinsic motivation and perceived effort (r = .33, p < .05). There were also moderate and positive relationships between perceived effort and identified regulation (r = .38, p < .05), academic achievement and identified regulation (r = .31, p < .05), and perceived effort and introjected regulation (r = .42, p < .05). Finally, there was a low and positive relationship between perceived effort and external regulation (r = .20, p < .05), academic achievement and external regulation (r = .26, p < .05) as

13

well as a moderate and negative relationship between amotivation and perceived effort (r = -.46, p < .05), and a low and negative relationship between amotivation and academic achievement. Table 5 Pearson Product-Moment Correlation among Outcomes of Self-Determination (n = 114) Outcome Intrinsic Motivation

Satisfaction Before .20*

Satisfaction Later .23*

Perceived Effort .33*

Academic Achievement .20*

Identified Regulation

-.10

.03

.38*

.31*

Introjected Regulation

.00

.15

.42*

.09

-.06

-.02

.20*

.26*

.01

-.14

-.46*

-.26*

External Regulation Amotivation *p < .05

Conclusions/Implications Motivational Profile The sample, consisting of slightly more females, was high in extrinsic motivation, averaging 6 on a 7-point scale. Females were more likely to attend school because of external factors imposed on them either by self (i.e. introjected regulation) or by an outside influence (i.e. external regulation). This was followed by a moderate range for intrinsic motivation, averaging 5 on a 7-point scale. Finally, the sample was moderately low in amotivation, averaging 3 on a 7point scale; however, males were more likely to be amotivated. These results support findings by Ratelle et al. (2007) that found that in a study of Canadian high school students, females reported higher levels of introjected regulation, and lower levels of amotivation. Contrary to the findings of the Ratelle et al. study, females in this sample were not higher in intrinsic motivation. Although not statistically different, the males in this sample reported more intrinsic motivation to attend school. Further exploration of the sample is needed to understand this finding. However, if the sample associates motivation to attend school with motivation to attend this comprehensive agricultural high school, then gender bias about agriculture may be a contributing factor in the contradicting findings. The sample’s low rating in amotivation stressed their desire to want to attend school and intent to learn. Although students did report a moderate level of intrinsic motivation, they were strongest in extrinsic motivation. This can be interpreted as the students are most likely motivated to go to school because of external inducements. This includes the desire to receive scholarships to further their education, the perception that the agriculture program will help them develop desired skills, making parents happy, and so forth. The presence of these controlled motives indicate that students are able to adapt to the school setting which would yield academic achievement but this adaptation is contingent on the external inducements of value to the student

14

being present. The “C” grade point average for the sample confirmed that the sample had not internalized the educational context and therefore was not fully self-determined. When asked about influences in the decision to enroll in the agricultural high school, the sample reported self-selection followed by family decision as the strongest influences in their decision. This is promising because it indicates a level of perceived autonomy with choosing which high school to attend. Students who perceive autonomy support (autonomy and relatedness) in educational decisions tend to be more engaged and persistent with difficult tasks related to those educational decisions (Reeve & Jang, 2006). That is, students who perceive they have a decision in their education and that decision is support by influential adults tend to display more self-determined behaviors. Fortunately, because the students in this sample perceived they had full autonomy in the decision to attend or was very influential in the family’s decision, overall they have a more positive perception of school and show more persistence in academic tasks. This is validated by the sample’s indication that they were satisfied with their decision to enroll and persisted in academic tasks related to agriculture. Motivational Relationships The relationships identified between factors influencing autonomy and outcomes of selfdetermination supported the literature on self-determination. Students who self-selected to attend the agricultural school also indicated that intrinsic motivation as well as introjected regulation was the major motivation for attending school. In addition, students who reported that the decision to attend the school was a family decision were also more intrinsically motivated as well as externally regulated. This may indicate that some of these urban students enrolled into the agricultural high school because they had an intrinsic interest in learning about agriculture. The most common responses for those students who had an intrinsic interest in agriculture were because they were interested in a particular career (e.g. veterinarian or landscape designer) or because they wanted to learn about something different. However, those who were identified with introjected regulation self-selected because they perceived it was the right thing to do. Possible reasons include: older siblings already enrolled and financial alternative for residents who would typically attend one of the three parochial schools in the surrounding neighborhood. In both instances the student made the choice, however, the choice was not motivated by an inherent interest. Similarly those who identified with external regulation may have chosen the school because of its reputation as a safe public high school, better educational resources, track record with academic scholarships and student acceptance rate into college, or incentives from the family. These findings confirm factors identified by urban students in Philadelphia who reported that recruitment activities, interest in animals, agricultural career aspirations, and parental influence accounted for more than half of the reason for enrollment in an urban agriculture program (Esters & Bowen, 2004). Conversely, students who reported their mother or father made the decision to go to the school were more likely amotivated. These students were not satisfied with that decision and reported exerting less effort on academic tasks related to agriculture.

15

Academic Achievement Finally, in terms of academic achievement, the results were consistent with the literature on SDT. Those participants who were more self-determined, as measured by intrinsic motivation and identified regulation were more likely to have a higher GPA. The findings indicate that the participants who were motivated by identified regulation, although may not have been interested in agriculture, persisted in academic tasks because they understood and valued the opportunity the school affords them in terms of accomplishing their future career aspirations. The significant relationship between academic achievement and external regulation confirms that external inducements such as grades, scholarship, and awards can influence some individuals to persist at academic tasks. In addition, the significant negative relationship between amotivation and academic achievement supported the literature and confirmed the importance of identifying participants who are amotivated early in their academic career in order to intervene before it is too late. Recommendations Student engagement is a very hot topic in many educational disciplines (e.g. career and technical education, special education, primary education, post-secondary distance learning, and physical education). Researchers (Anderson, 2007; Anderson, Torres, & Ulmer, 2007; Fredricks et al., 2004; Ryan & Powelson, 1991) proposed that a possible solution for increasing student engagement is to create an educational environment that addresses students’ innate motivation to learn. Lessons must be relevant to the intended audience by identifying both current and future utility. Based on this premise, the following recommendations have been offered. Recommendation 1. School administration, counselors and agriculture instructors at CHSAS should use this information to facilitate a discussion on how to better serve the students. Mainly, what strategies can be incorporated to turn the students’ external motivation toward school and studying agriculture into intrinsic motivation (internalization)? The overall goal of this is to improve academic achievement and increase retention of urban students in agriculture and related sciences after graduation from high school. An example of a targeted approach would be to provide a clear message to students of the current importance of agriculture in their lives and the impact they can have on society with the knowledge they are receiving. Often students are instructed with the premise that the information they are receiving will be important in the future, however, research shows that students become both engaged and more persistent with academic tasks when they know the current importance and relevance of that information and place personal value in knowing that information (Blank, 1997; Mitsoni, 2006; Sull, 2006). Recommendation 2. Agriculture teachers should continue to explore ways of providing autonomy to students in educational settings; particularly focusing on interventions that target amotivated and extrinsically motivated students and move them more towards self-determined behaviors (e.g. student-centered instruction and choice in agricultural courses and concentrations).

16

Recommendation 3. Further research should be conducted to explore the following topics: •

Can the motivational profile be used to predict student academic achievement based on overall grade point average, grade point average for agriculture courses, and grade point average for core courses (i.e. math, science, and language arts)? The purpose is to gather information in support of increasing elective courses that are content rich (e.g. math, science, and reading) rather than eliminating them to make room for more core requirements;

•

Evaluate the academic outcomes of initiatives mentioned in recommendation 2. Students’ achievement in a magnet school like CHSAS may be attributed not only to academic aptitude, but perceived influence in the decision to attend the school and curriculum choice. At CHSAS students not only choose to “opt out” of their neighborhood school but choose among five agricultural career pathways to study while attending the school. By accounting for academic aptitude, does the autonomy supportive initiative account for variation in academic achievement?

•

Are students in other agricultural education programs intrinsically motivated to learn about agriculture or do they see it as a means to an end (extrinsically motivated)? The purpose is to further validate this line of inquiry so that interventions can be created that elicit positive motivational outcomes on engagement and academic achievement; and

•

What are the outcomes of students who perceive support (parents, teachers, counselors, and friends) in this decision to enroll in an agricultural education program versus students who do not perceive support? Mainly, do these students stay in the program throughout their high school career and do they major in agriculture in college or take a related job? The purpose is to gather information on the impact of autonomy support (i.e. autonomy and relatedness) on the viability of secondary agricultural education programs. References

Abrahamson, L., Seligman, Y., & Teasdale, M. (1978). Learned helplessness in humans: Critique and reformulation. Abnormal Psychology, 87, 49-74. Ames, C.A. (1990). Motivation: What teachers need to know. Teachers College Record, 91(3), 409-421. Anderman, L.H., & Midgley, C. (1998). Motivation and middle school students. Champaign, IL: ERIC Clearinghouse on Elementary and Early Childhood Education. Anderson II, J. A. (2007). Effect of problem-based learning on knowledge acquisition, knowledge retention, and critical thinking ability of agriculture students enrolled in urban schools. Unpublished doctoral dissertation, University of Missouri, Columbia.

17

Anderson II, J. A., Torres, R. M., & Ulmer, J. D. (2007). An assessment of the leadership development needs of urban agriculture students. Proceedings of the 2007 American Association for Agricultural Education National Conference, Minneapolis, MN. Bandura, A. (1977). Self-efficacy theory: Toward a unifying theory of behavioral change. Psychological Review, 84, 191–215. Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Englewood Cliffs, NJ: Prentice Hall. Bandura, A. (1989). Social cognitive theory. In R. Vasta (Ed.), Annals of child development (6, pp. 1-60). Greenwich, CT: JAI Press. Baumeister, R. F., & Heatherton, T. F. (1996). Self-regulation failure: An overview. Psychological Inquiry, 7, 1–15. Blank, W. (1997). Authentic instruction. In W.E. Blank & S. Harwell (Eds.), Promising practices for connecting high school to the real world (pp. 15-21). Tampa, FL: University of South Florida. (ERIC Document Reproduction Service No. ED 407 586). Burkett, E. (2002) Another planet: A year in the life of a suburban high school. New York: Harper Collins. Carver, C. S., & Scheier, M. F. (1999). Themes and issues in the self-regulation of behavior. In R. S. Wyer, Jr. (Ed.), Advances in social cognition: Vol. XII. Perspectives on behavioral self-regulation. Mahwah, NJ: LEA. Davis, J. A. (1971). Elementary survey analysis. Englewood, NJ: Prentice Hall. de Charms, R. (1968). Personal causation: The internal affective determinants of behavior. New York: Academic Press. de Charms, R. (1976). Enhancing motivation: Change in the classroom. New York: Irvington. Deci, E. L. (1971). Effects of externally mediated rewards on intrinsic motivation. Journal of Personality and Social Psychology, 18, 105-115. Deci, E. L. (1975). Intrinsic motivation. New York: Plenum. Deci, E. L., Eghrari, H., Patrick, B. C., & Leone, D. R. (1994). Facilitating internalization: The self-determination perspective. Journal of Personality, 62, 119–142. Deci, E. L., & Ryan, R. M. (1985). Intrinsic motivation and self-determination in human behavior. New York: Plenum.

18

Deci, E. L., & Ryan, R. M. (1991). A motivational approach to self: Integration in personality. In R. Dienstbier (Ed.), Nebraska symposium on motivation: Vol. 38. Perspectives on motivation (pp. 237–288). Lincoln, NE: University of Nebraska Press. Deci, E. L., & Ryan, R. M. (2000). The “what” and “why” of goal pursuits: Human needs and the self-determination of behavior. Psychological Inquiry, 11(4), 227-268. Dewey, J. (1938). Experience and education. New York: Collier. Esters, L.T., & Bowen, B.E. (2004). Factors influencing enrollment in an urban agricultural education program. Journal of Career and Technical Education, 21(1). Evelein, F., Korthagen, F., & Brekelmans, M. (2008). Fulfillment of the basic psychological needs of student teachers during their first teaching experiences. Teaching and Teacher Education: An International Journal of Research and Studies, 24(5), 1137-1148. Fredricks, J. A., Blumenfield, P. B., Friedel, J. & Paris, (2004). School Engagement: Potential of the concept, state of the evidence. Review of Educational Research, 74, 59-109. Geen, R. (1994). Human motivation: A psychological approach. Wadsworth Publishing. Kruglanski, A.W. (1978). Endogenous attribution and intrinsic motivation. In M.R. Lepper & D. Greene (Eds.), The hidden costs of reward: New perspectives on psychology of human motivation, 85-107. Hillsdale, NJ: Lawrence Erlbaum. Hong, E., & Milgram, R. M. (2000). Homework: Motivation and learning preference. Westport, CT: Greenwood. Hong, Tomoff, Wozniak, Carter, & Topham, (2000). A cross-cultural examination of the kinds of homework children prefer. Journal of Research and Development in Education 34(1), 28-39. Kirschemnbaum, D. S. (1987). Self-regulation failure: A review with clinical implications. Clinical Psychology Review, 7, 77–104. Kruglanski, A.W. (1978). Endogenous attribution and intrinsic motivation. In M.R. Lepper & D. Greene (Eds.), The Hidden Costs of Reward: New Perspectives on Psychology of Human Motivation, 85-107. Hillsdale, NJ: Lawrence Erlbaum. Kushman, J.W., Sieber, C., & Heariold-Kinney, P. (2000). This isn't the place for me: School dropout. In D. Capuzzi & D.R. Gross (Eds.), Youth at risk: A prevention resource for counselors, teachers, and parents (3rd ed., pp. 471-507). Alexandria, VA: American Counseling Association.

19

Lumsden, L.S. (1994). Student motivation to learn. Eugene, OR: ERIC Clearinghouse on Educational Management. McClelland, D. (1987). Human motivation. Cambridge, NY: Cambridge University Press. Mitsoni, F. (2006). “I got bored when we don’t have the opportunity to say our opinion”: Learning about teaching from students. Educational Review, 58(2), 159-170. Pelletier, L. G., Fortier, M. S., Vallerand, R.J, & Bri’ere, N. M. (2001). Associations among perceived autonomy support, forms of self-regulation, and persistence: A prospective study. Motivation and Emotion, 25(4), 279 - 306. Pintrich, P. R., & Schunk, D. H. (2002). Chapter 5: The role of goals and goal orientation. Motivation in Education: Theory, Research, and Application (2nd edition), 190-242. Upper Saddle River, NJ: Merrill-Prentice Hall. Pope, D. (2002). Doing School: How we are creating a generation of stressed-out, materialistic, and miseducated students. New Haven, CT: Yale University Press. Rader, L.A. (2005). Goal setting for students and teachers: Six steps to success. Clearing House, 78(3), 123-126. Reeve, J., & Jang, H. (2006). What teachers say and do to support students’ autonomy during a learning activity. Journal of Educational Psychology, 98(1), 209-218. Rumberger, R. W. (1987) High school dropouts: A Review of issues and evidence. Review of Educational Research, 57, 101-121. Rutter, K.L., Smith, B.P., & Hall, H.C. (2002) Motivational needs of family and consumer sciences education students. Journal of Career and Technical Education, 18(2). Ryan, R. (1993). Agency and organization: Intrinsic motivation, autonomy and the self in psychological development. In J. Jacobs (Ed.), Nebraska symposium on motivation (Vol. 40, pp. 1–56). Lincoln, NE: University of Nebraska Press. Ryan, R. M., & Powelson, C. L. (1991). Autonomy and relatedness as fundamental to motivation and education. Journal of Experimental Education, 60(1), 49-66. Stanford, S., & Couch, S. (1985). Attitudes of members and advisors toward competition and recognition in Future Homemakers of America. Journal of Vocational Home Economics Education, 3(2), 58-69. Sull, E.C. (2006). Want to motivate your students? Just go through the back door! Online Classroom, 6(2), 81-99.

20

Sullivan, P., Tobias, S., & McDonough, A. (2006). Perhaps the decision of some students not to engage in learning mathematics in school is deliberate. Educational Studies in Mathematics, 62(1), 81-99. Vallerand, R. J., Blais, M. R., Briere, N. M., & Pelletier, L. G. (1992). Academic Motivation Scale. Vallerand, R. J., & Bissonnette, R. (1992). Intrinsic, extrinsic, and amotivational styles as predictors of behavior: A prospective study. Journal of Personality, 60(3), 599-620. Wolfe, E.W. (1996). Student reflection in portfolio assessment. New York, NY: National Council on Measurement in Education. Zimmerman, B. J. (1990). Self-regulated learning and academic achievement: An overview. Education Psychologist, 21, 3-17. Authors James C. Anderson II is a Visiting Assistant Professor of Agricultural Education in the Department of Human & Community Development at the University of Illinois at UrbanaChampaign, 155 Bevier Hall, Urbana, IL, 61801. Email: [email protected]. Phone: 217-2440285. Fax: 217-244-7877. Robert M. Torres is a Professor in the Department of Agricultural Education at University of Missouri-Columbia, 126 Gentry Hall, Columbia, MO, 65211. Email: [email protected]. Phone: 573-882-7451. Fax: 573-884-4444.

21

WEST VIRGINIA AGRICULTURAL EDUCATION TEACHERS PERCEPTIONS ON INVOLVING STUDENTS WITH EXCEPTIONALITIES IN AGRICULTURAL CLASSROOMS AND LABORATORIES Deborah A. Boone, West Virginia University Ashley Watts, West Virginia University Harry N. Boone, Jr., West Virginia University Stacy A. Gartin, West Virginia University The purpose of this study was to examine West Virginia Agricultural Education Teachers perceptions on involving students with exceptionalities in agricultural classrooms and laboratories. This study examined whether teachers felt confident, well-prepared, and if other students interact well with students with exceptionalities. This study also sought to determine if agricultural teachers felt they had adequate training to work with students with exceptionalities or if additional training was needed. The majority of teachers agreed that including students with exceptionalities in the classroom fosters understanding for diverse populations. A majority of teachers have seen an increase in students with exceptionalities in their classrooms. Agricultural teachers feel confident and well-prepared to work with students with exceptionalities; however, they did not feel confident or well-prepared to work with students with exceptionalities when they first started teaching agriculture. A majority of teachers feel classes/trainings should be required to prepare teachers to work with students with exceptionalities. Introduction At the turn of the 21st century, Rufus Stimson became concerned over how agricultural education was taught (Moore, 1988). Stimson worked diligently at installing the project concept, which is a program where students would learn agriculture at school and apply those concepts on their home farms (Moore, 1988). The agricultural science education program progressed further in 1917 when congress passed the Smith-Hughes Act which established vocational agricultural classrooms (Patterson, n.d.). The Vocational Education Act of 1984 often referred to as the Perkins Act, authorizes federal funds to support agricultural education programs (National Information Center for Children and Youth with Disabilities, 1996). This law is vital to agricultural educators because it requires that agricultural education be provided for students with exceptionalities. The Perkins Act states that individuals who have exceptionalities must be provided with equal access to every aspect agricultural education offers (National Information Center for Children and Youth with Disabilities, 1996). In 1990, Congress passed the Individuals with Disabilities Education Act (IDEA) which is also known as P.L. 101-476. The IDEA makes it possible for individual states to receive federal funding for students with exceptionalities (National Information Center for Children and Youth with Disabilities, 1996).

22

In 2001, President George W. Bush signed the No Child Left Behind Act (NCLBA) which proposes schools be accountable for all students, including those with exceptionalities, to meet high standards of learning (The White House, n.d.). NCLBA requires yearly standard testing, and the consequences for failing to make progress for students with exceptionalities includes: receiving assistance, then, if needed, undergoing corrective action. If no improvement is made in three years, students with exceptionalities may transfer to higher-performing schools, or have the option of receiving educational services from whomever they choose (The White House, n.d.). For the academic year of 2007-2008, there are currently 281,735 students enrolled in West Virginia Public Schools. Of that number, 51,669 are students with exceptionalities. (West Virginia District Special Education Data Report: 2007-2008, n.d.). There are currently 5,000 plus students enrolled in Agricultural Education in the state of West Virginia and 4,600 plus FFA members in 43 counties (National FFA, n.d.). Seventeen percent of all students enrolled in West Virginia Public Schools are students with exceptionalities (West Virginia District Special Education Data Report: 2007-2008, n.d.). The mission of Agricultural Education is to prepare and support individuals for careers, build awareness, and develop leadership for the food, fiber, and natural resource systems. The FFA mission states that FFA makes a positive difference in the lives of students by developing their potential for premier leadership, personal growth, and career success through agricultural education (National FFA, n.d.). Legislation requires FFA advisors to provide equal access to services and programs for all people, regardless of their disability (Bridging Horizons, 1996). Agricultural Educators have the responsibility of dealing with emotional impairments, hearing impairments, developmental disabilities, learning disabilities, mental disabilities, visual impairments, and physical disabilities (Bridging Horizons, 1996). In 1962, Kirk defined students with exceptionalities as: the child who deviates from the average or normal child (1) in mental characteristics, (2) in sensory abilities, (3) in neuromuscular or physical characteristics, (4) in social or emotional behavior, (5) in communication abilities, or (6) in multiple handicaps to such an extent that he requires a modification of school practices, or special educational services, in order to develop to his maximum capacity. (p. 4) Kessel (2006) found that agricultural education programs are becoming a popular course for the inclusion of students with disabling conditions, but little research has been conducted to assess teacher confidence and knowledge regarding special education and teaching diverse populations in agricultural education classrooms and laboratories. With inclusion comes the consideration of how to assess students with exceptionalities in the classroom. Students with learning disabilities cannot be denied participation in the agricultural classroom. Helt (1975) stated that future agricultural teachers must assume the responsibility of being prepared to accommodate students with exceptionalities in their classes. He believed that agricultural educators should provide the best possible pre-service preparation for new teachers

23

that are aimed at meeting state and federal guidelines (Helt, 1975). During the 1977-1978 academic years at North Dakota State University, a course entitled “Teaching Vocational Students with Special Needs” was offered in the agricultural education program. The goal was to teach future teachers to become more empathetic toward all students, including the gifted, disadvantaged, and handicapped. It was also geared to help teach agricultural teachers to develop Individualized Education Plan (IEP) for all included students with exceptionalities (Helt, 1975). Curtis (1975) addressed that the vocational amendments of 1968 (which emphasizes the need for new programs and facilities to serve the handicapped and disadvantaged) put students with exceptionalities in the back of the classroom when it came to the development of the human resource potential for the student. A concern acknowledged by Curtis (1975) is that teaching students with exceptionalities degrades the quality of instruction provided to other students. Curtis (1975) also stated that a possible solution to this concern is the quality of the teacher. All students respond best when teachers relate instruction to real life, something agricultural education aims to do (Curtis, 1975). Curtis (1975) suggested that emphasis on teaching students with exceptionalities can result in an improved program for everyone. Barrett (1975) stated that serving students with exceptionalities is not new for agricultural educators; agricultural teachers have always taught students with exceptionalities without realizing it. The main reason for this statement is the lack of identification of students with exceptionalities. Any student that is not succeeding or cannot succeed in an agricultural class without special help or any student’s disability that is a contributing factor to his/her lack of success in that particular class is defined as a student with exceptionalities (Barrett, 1975). Agricultural teachers have already been implementing students with exceptionalities in their classrooms; any students that needs any special help or the teacher changes any curriculum to fit that student’s need is considered a student with exceptionalities (Barrett, 1975). Woehler (1975) suggests that one of the most important things an agricultural teacher can do is be a motivated teacher, they should be enthusiastic and imaginative. A disruptive student is seeking attention and will continue to do so until his/her emotional needs are met (Woehler, 1975). Walls (1975) also stated that teachers should possess certain characteristics for working with students with exceptionalities. Some of these qualities include: competence in the subject matter, ability to create a positive learning environment, ability to properly diagnose specific exceptionalities, ability to manage a classroom with students with exceptionalities, and ability to modify classroom activities for students with exceptionalities. Hanson (1975) believes the challenge of teaching students with exceptionalities can be deeply frustrating and highly satisfying. In order to be successful, the agriculture teacher must learn to accept the student for what he/she is. Social, economic, and ethnic upbringings have molded this student. According to Hanson, (1975) exposing students with exceptionalities to new standards and philosophies can introduce these students to a whole new way of life. To teach students with exceptionalities, teachers must be concerned with more than the subject matter. To reach students with exceptionalities, instruction should be made applicable. The most important factor in teaching students with exceptionalities is to learn to empathize with them. Bobbitt (1975) articulated that the philosophy of individual development is the core purpose of the agricultural sciences program. If this philosophy is assumed, then working with

24

students with exceptionalities is not something extra to do, but something that is essential to the program. Bobbitt (1975) also argued that there is no greater reason for existence of vocational programs then to assist those who need it most. The reason for implementing more agricultural programs is to assist the less qualified in competing in the labor market (Bobbitt, 1975). Fisher (1999) found that including students with exceptionalities adds value to the education experience for students without exceptionalities because the experience has enticed them to think about their values, beliefs, and own behaviors. In contrast to Fisher; Carter, Hughes, Guth, & Copeland, (2005) found that students with exceptionalities typically did not interact with their general peers. The inclusion competencies most in need of strengthening among the teachers were: understanding special education regulations, understanding different levels of special education services, understanding different levels of disabilities, and understanding the social needs of special education students (Andreasen, Seevers, Dormody, & Vanleeuwen, 2003). The research in secondary agricultural education related to teaching students with exceptionalities indicates that agricultural education teachers perceive low ability, but high importance of competencies in teaching students with exceptionalities. Agricultural education teachers can expect students with exceptionalities to represent a sizable proportion of the total population of students in their program (Andreasen, et al., 2003). According to Andreasen, et, al. (2003) top special education in-service topics identified by the teachers were: 1) making modifications to reach Individualized Educational Plans (IEPs), 2) evaluating learning, and 3) making classroom modifications. Daniels and Walker (1975) indicated that there are cases when children with exceptionalities should be prevented from taking certain agriculture classes because of safety for themselves and the safety of others. However, the fact remains that if an administrator, counselor, or teacher discourages children with exceptionalities away from certain courses, they are breaking the law. The law states that all people have the basic right to an education, treatment, and job opportunities, all people have the right to due process of law as provided under the fourteenth amendment of the Unites States Constitution, and each state must spend 25% of its 1968 Vocational Education Act Amendment funds for the handicapped and disadvantaged. Cicchetti (1975) declared that one of the most important challenges to public education, not just agricultural education, is occupational education for students with exceptionalities. A prepared individual with exceptionalities is an asset to society, rather than a liability (Cicchetti, 1975). Walls (1975) stated that teaching students with exceptionalities has been a challenge to teachers for many years. Congress discovered that not enough emphasis was given to students with exceptionalities; therefore the 1963 Vocational Education Act mandated that each state would not use less than 15 percent of its funds for the disadvantaged. Agricultural education has always been geared to helping disadvantaged individuals; the programs were truly never designed to meet specific needs of students with exceptionalities (Walls, 1975). Walls (1975) suggested reducing class size, using conference periods, using specific equipment, materials,

25

visuals, and demonstrations, working with students with exceptionalities on weekends and after school, and field trips to help students with exceptionalities succeed in the classroom. Kossar, Mitchem, & Ludlow (2005) stated that the No Child Left Behind Act (NCLBA) and the Individuals with Disabilities Education Improvement Act (IDEIA), public schools must bring all students to the level required on state content tests. A study conducted by Kossar et al. (2005) indicated that a majority of the participants believed that NCLBA would have a negative impact on rural schools. Stating that rural schools would have difficulty meeting the requirements of NCLBA in the area of special education., Kossar et al. (2005) stated that rural schools foresee shortcomings in meeting the NCLBA requirements because rural schools have difficulty retaining qualified educators. Hammond and Ingalls (2003) stated that rural schools could have a high number of teachers on emergency certification to work with students with exceptionalities. They (2003) also stated that rural teachers may not have access to classes/trainings on working with students with exceptionalities. A concern for rural schools with regards to NCLBA and special education is how rural schools will access trainings to ensure teachers are fully qualified to work with students with exceptionalities (Kossar et al, 2005). Hammond and Ingall’s (2003) study showed that a high percentage of rural teachers had negative attitudes towards programs implementing inclusion. Cicchetti (1975) observed that some students with exceptionalities are lacking in “survival skills”, which include social responsibility, reliability, skills needed for productivity, and good work habits. Cicchetti’s (1975) argument is that any students with exceptionalities that can acquire these survival skills are then partially prepared for agricultural endeavors. Bobbitt (1975) also brought up the issue that agricultural education has been considered a dumping ground by some for students that perhaps could not excel in other classes. A true dumping ground is where students are placed into agricultural classes that have no interest in agriculture, not because the student may have exceptionalities (Bobbitt, 1975). Gauper (1975) declared that schools started to trap students into a structured classroom with no regard to their interests, skills, and limitations. Because of this, the learning process was slowed for the higher achiever and frustrating for the non-academic. Problem Statement Given the laws and the push for inclusion of students with exceptionalities into the classroom, one can assume that some of these students will be involved with agricultural sciences education and the FFA. Since agricultural science education and the FFA are an integrated part of the public school system, access cannot be denied to any individual with exceptionalities who would like to participate. P.L. 94-142, The Education for all Handicapped Children Act of 1975 states that a "free appropriate education" is offered to students with exceptionalities (National Information Center for Children and Youth with Disabilities, 1996). Since this act, P.L. 98-524, and the Vocational Education Act of 1984 are in effect, it is important that agricultural educators understand the needs of students with exceptionalities and are prepared to include these students into their classrooms and laboratories.

26

Purpose /Objectives The purpose of this study was to determine the perceptions of West Virginia agricultural educators on involving students with exceptionalities in agricultural classrooms and laboratories. The objectives of the study are reflected in the following research questions: 1. What is the nature and extent of students with exceptionalities in West Virginia agricultural education classrooms and laboratories? 2. Do agricultural education teachers feel prepared to work with students with exceptionalities in their classrooms and laboratories? 3. Have agricultural educators adapted/changed curriculum and/or facilities to accommodate students with exceptionalities? 4. Would additional training improve the way agricultural educators work with students with exceptionalities? 5. How do agricultural educators feel students with exceptionalities are viewed by other students? Definitions of Terms For the purpose of this study, the following definitions of terms were used: Exceptionalities Socially Maladjusted (Behavior): Students who are socially maladjusted typically display a persistent pattern of willful refusal to meet even minimum standards of conduct. Their behavior and values are often in conflict with society’s standards. They exhibit a consistent pattern of antisocial behavior without genuine signs of guilt, remorse, or concern for the feelings of others. Physical: Students with physical exceptionalities display limited mobility (ex. Missing limbs, limited to a wheelchair), special health problems (ex. Heart problems). Mental: Students with academic exceptionalities display a persistent pattern of reading and writing difficulties, comprehension difficulties, and exhibit slowed cognitive processes. Methods/Procedures Research Design A descriptive research design was selected to collect data from agricultural educators. Ary, Jacobs, Razavieh, and Sorenson (2006) defined descriptive educational research as: “acquiring dependable and useful information, to discover principles or interpretations of behavior that can be used to explain, predict, and control events in educational situations.” The target population consisted of 91 West Virginia Agricultural Educators, employed during the spring of 2008. A census was conducted of all Agricultural Educators listed in the 2007-2008 West Virginia Secondary Agriculture Teachers and Schools Directory. Frame error was avoided by using an official list of agricultural education teachers maintained by West Virginia University’s Agricultural and Extension Education Department. The use of a census eliminated the possibility of selection and sampling errors. Instrumentation The instrument used for this study was a mailed questionnaire. Measurement error was reduced by establishing the validity and reliability of the data collection instrument. The

27

instrument was presented to a panel of experts to establish its content and face validity. The panel consisted of faculty members in Agricultural and Extension Education and Special Education at a land grant University. Members of the panel had experience in teaching, extension, research and special education. They concluded that the instrument had content and face validity. The final data set was used to determine the instrument’s reliability. The 26 Likert items were tested for reliability by using the Spearman-Brown split-half coefficient. Reliability was found to be exemplary with a Spearman-Brown coefficient of .83. The instrument was found to be reliable. Data Collection Procedure Dillman’s Total Design (2005) was used to collect data. The questionnaire and cover letter were mailed to each individual in the target population along with a stamped self-addressed return envelope. A second questionnaire was sent to all non respondents two weeks later. Returned questionnaires were examined and entered into an excel spreadsheet. The data was transferred to the personal computer version of the Statistical Package for the Social Sciences (SPSS). Levels of significance were set a priori at .05. a Sex coded: female = 0, male = 1; b discipline: 0 = social, 1 = natural/physical.

p-value .14 .97 .58

.01

Approximately nine percent of the variance in perceived creative teaching behavior can be explained by the linear combination of age, sex, and discipline (F(3, 34) = 1.12; p> .05) (see Table 5). However, the regression model was not significant. The null hypothesis for the first hypothesis was there is no difference between discipline (natural/physical or social science) and level of creative teaching behaviors, as perceived by students. A non-directional, independent samples t-test was calculated to test the first null hypothesis. Levene’s Test for Equality of Variances was conducted and the variances for student perceptions of creative teaching behaviors (p = .38), were calculated. Due to non-significant variances (p > .05), equal variances were assumed for each of the variables and evaluated for differences (see Table 6). Table 6 Independent Samples t Test of Differences Between Disciplines and Students’ Perceived Creative Teaching Behaviors of Instructors Discipline

n

Mean

SD

t-value

p-value

Natural/physical

25

6.00

.40

1.88

.07

Social science

15

5.57

.82

Differences between disciplines (natural/ physical or social science) and level of creative teaching behaviors (p = .07) were not statistically significant. Therefore, the null hypotheses that no differences existed (p> .05) between disciple and level of creative teaching behaviors, as perceived by students, was accepted. The null hypothesis for hypothesis two was that no relationships exist between teaching experience and level of creative teaching behaviors, as perceived by students. Five years of 132

teaching experience was chosen to distinguish between novice and veteran teaching experience because that is the experience level at which CAFNR classifies faculty for its annual teaching awards. CAFNR classifies novice instructors as those with five years or less of experience. Therefore, this study followed CAFNR’s distinction between novice and veteran instructors and forced teaching experience into a dichotomous variable to examine hypotheses two. A non-directional, independent t-test was calculated to test the second null hypothesis. Levene’s Test for Equality of Variances was conducted and the variances for student perceptions of creative teaching behaviors (p = .20), was calculated. Due to non-significant variances (p > .05), equal variances were assumed for each of the variables and evaluated for differences (see Table 7). Table 7 Independent Samples t Test of Differences Between Experience and Creative Teaching Behaviors of Instructors, as Perceived by Students Teaching Experience

n

Mean

SD

t-value

p-value

> 5 years

31

5.56

.65

2.03

.05*

< 5 years * p ≤ .05.

9

5.00

.94

There was a significant difference between creative teaching behaviors of experienced and in-experienced CAFNR instructors when evaluated by students (p = .05). Therefore, the null hypotheses that stated no differences existed between teaching experience and creative teaching behaviors, as perceived by students, was not accepted. Null hypothesis three stated that no differences existed between sex and level of creative teaching behaviors, as perceived by students. A non-directional, independent samples t-test was calculated to test the second null hypothesis. Levene’s Test for Equality of Variances was conducted and the variance for student perceptions of creative teaching behaviors (p = 1.0) was calculated. Due to non-significant variances (p > .05), equal variances were assumed for each of the variables and evaluated for differences (see Table 8). Table 8 Independent Samples t Test of Differences Between Sex and Creative Teaching Behaviors of Instructors, as Perceived by Students Sex

n

Mean

SD

t-value

p-value

Male

27

5.51

.76

-.95

.35

Female

13

5.27

.73

Students’ perceived creative teaching behaviors of CAFNR instructors (p = .35) was not statistically different when compared by sex. Therefore, the null hypotheses that no differences existed between sex and level of creative teaching behaviors, as perceived by students, was accepted. 133

Conclusions, Recommendations, and Implications Students perceive that instructors in CAFNR demonstrate creative teaching behaviors. However, the range in scores indicates that students varied considerably in their perceptions of instructor creativity in the classroom. Considering the range of scores associated with student perceptions of instructors’ use of creative teaching behaviors, it is apparent that students are capable of evaluating creativity in the classroom. This conclusion is a valuable step in research about creative teaching behaviors demonstrated by teachers. Documentation of students’ perceptions of creative teaching does not appear to be available in previous literature. Students believe their instructors are effective teachers. Students agreed that their instructors displayed clarity, variability, opportunity to learn, task oriented, and enthusiasm in their teaching. Enthusiasm was the most frequently reported effective teaching construct demonstrated by college faculty while variability was least observed by students. These findings suggest that students generally believe their teachers demonstrate enthusiasm in the classroom. The ranking of the variability construct suggests students may not be exposed to a variety of instructional methods. While students agreed that CAFNR instructors demonstrated effective teaching characteristics, the range in scores also suggests students vary in their perceptions of instructors. This indicates students can discern between effective and non-effective instruction. If students can, in fact, differentiate between effective and non-effective instructors, what behaviors do they identify as most important to effective teaching? It is also interesting to note student perceptions of clarity demonstrated in the learning environment. Variability and clarity had the greatest range in scores, which again suggests students are capable of distinguishing when effective teaching behaviors occur. Could high levels of agreement with some constructs, such as enthusiasm, actually reduce other areas, such as clarity? Additional research should be conducted to determine which methods instructors use in the learning environment. Defining and identify teaching methods which improve clarity should also be the focus of future research. Finally, faculty development programs should be designed to address increasing variability and clarity in the learning environment. Students consider creative instructors to be effective instructors. The strong, positive correlation between these two variables found in this study supports previous findings comparing creative and effective teachers (Anderson, 2002; Bain, 2004; Croply, 1967, 2001; Davidovitch & Milgram, 2006; Esquivel, 1995; Fasko, 2000-01; Newcomb et al., 1993; Torrance, 1981, 1995). Creative teaching behavior constructs should be compared to each characteristic of effective teaching to provide more specific methods to improve effective teaching. Discipline is not a factor to consider when addressing creativity of CAFNR university instructors. Perhaps due to the research environment found in both natural/physical and social sciences within universities, creativity does not appear to differ. It would appear appropriate to address all instructors, regardless of discipline, in future research. In addition, educational opportunities to enhance creativity may be appropriately targeted to both natural/physical and social science disciplines.

134

The consistency of creativity across disciplines may also provide new areas for understanding between the vastly different disciplines. In addition, the ability to enhance effective teaching by increasing creative teaching behaviors should be examined. Do differences in effective teaching occur between disciplines? If creativity does not appear to vary between disciplines, would measures to enhance creative teaching behaviors be effective in both disciplines? There was a significant difference between students’ perceived creative teaching behaviors of CAFNR instructors and the experience of these instructors. Students suggested instructors with more than five years of teaching experience exhibit more creative teaching behaviors. Because students are the ultimate consumer of education offered by instructors, this is an important finding. Further research should address what specific behaviors experienced instructors demonstrated in the classroom which led to the significant differences in student perceptions of creative teaching behaviors. Additionally, would student perceptions of creative teaching be consistent with creative behaviors identified by instructors? Additional qualitative and quantitative research may shed light on these behaviors. Creative teaching behaviors, as perceived by students, do not appear to differ by the demographic characteristic of sex of the instructor. Sex does not appear to be a significant factor when examining creativity of college instructors. The apparent absence of a gender gap suggests both groups could be addressed by similar professional development opportunities regarding creativity. However, does effective teaching differ by sex? Would female students differ in their perceptions of effective teaching than their male counterparts? Further research should address the differences between sex and effective teaching. There was a significant difference between students’ perceived creative teaching behaviors of effective and non-effective CAFNR instructors. This suggests students are capable of identifying effective instructors and supports the previous findings that effective teaching is closely related to creative instruction. If effective teaching is directly related to creative teaching, then creative instructors may be more effective for students. Replication of this research should be conducted to support the findings between student perceptions of creative teaching behaviors demonstrated by instructors and student perceptions of effective teaching. Creative and effective teaching behaviors appear to be strongly connected for students. However, little is known about the factors which account for the creative teaching behaviors of instructors. Only nine percent of the variance in creative teaching behaviors, as perceived by students could be accounted for by the linear combination of age, sex, and teaching discipline. What other factors contribute to creative teaching behaviors of instructors? What characteristics of instructors account for additional variance in creative teaching behaviors? Although considerable research has been conducted on creativity, the influence of creative teaching behaviors offers an opportunity to increase effective teaching practices. Further research, including replication of this study, should be conducted to enhance findings on the impact creative teaching has upon effective teaching. Additional research should include identifying the value students place upon creative teaching behaviors and identifying specific behaviors which student perception of creative and effective teaching.

135

References Anastasi, A. (1976). Pychological testing. New York: Macmillan Publishing. Anderson, D. (2002). Creative teachers: Risk, responsibility and love. Journal of Education, 183 (1), 33-48. Ary, E., Jacobs, L.C., & Razavieh, A. (2002). Introduction to research in education (6th ed.). Belmont, CA: Wadsworth/Thomson Learning. Aschenbrener, M.A., Terry, R., Torres, R.M., & Smith, A.R. (2007). Creativity and job satisfaction. American Association of Agricultural Educators National Research Conference. Minneapolis, MN. Bain, K. (2004). What the best college teachers do. Massachusetts: Harvard University Press. Baker, M., Rudd, R., & Pomeroy, C. (2001). Relationships between critical and creative thinking. Journal of Southern Agricultural Education, 51(1), 173-188. Berry, W.D., & Feldman, S. (1985). Multiple regression in practice. Newbury Park, CA: SAGE Publications. Bleedorn, B. (2003). An educational track for creativity & other quality thinking processes. Lanham, Maryland: The Scarecrow Press, Inc. Bleedron, B. (2005). Education is everybody's business. Lanham, Maryland: Rowman & Littlefield Education. Chambers, J. (1973). College teachers: Their effect on creativity of students. Journal of Educational Psychology, 65 (3), 326-334. Cropley, A. (1967). Creativity. London: Longmans, Green & Co LTD. Cropley, A. (2001). Creativity in education & Learning. Bodmin, Cornwall: RoutledgeFalmer. Davidovitch, N., & Milgram, R.M. (2006). Creative thinking as a predictor of teacher effectiveness in higher education. Creativity Research Journal, 18 (3), 385-390. Davis, J. (1971). Elementary survey analysis. Englewood, New Jersey: Prentice-Hall. Dillman, D. (2007). Mail and internet surveys: The tailored design method (2 ed.). Hoboken, New Jersey: John Wiley & Sons, Inc. Esquivel, G. (1995). Teacher behaviors that foster creativity. Educational Psychology Review, 7 (2), 185-202. Fasko, D. J. (2000-2001). Education and creativity. Creativity Research Journal, 13 (3 & 4), 317-327. 136

Feldhusen, J.F., & Goh, E. E. (1995). Assessing and accessing creativity: An integrative review of theory, research, and development. Creativity Reserach Journal, 8 (3), 231-147. Fox, J., & Fox, R. (2000). Exploring the nature of creativity. Dobuque, IA: Kendall/Hunt Publishers. Friedel, C., & Rudd, R. (2005). Creative thinking and learning styles in undergraduate agriculture students. National AAAE Reserach Conference, (pp. 199-211). Goff, K.,& Torrance, E.P. (2002). Abbreviated Torrance test for adults manual. Illinoise: Scholastic Testing Service. Guilford, J. (1950). Creativity. American Psychologist, 444-454. Hocevar, D. (1981). Measurement of creativity: Review and critique. Journal of Personality Assessment, 45 (5), 450-464. MacKinnon, D. (1962). The nature and nurture of creative talent. American Psychologis, 17, 484-495. Massialas, B.G., & Zevin, J. (1983). Mulabar, Florida: Robert E. Krieger Publishing Company. Milgram, R. (1979). Perceptions of teacher behavior in gifted and nongifted children. Journal of Educational Psychology, 71 (1), 125-128. Miller, W.W., Kahler, A. A., & Rheault, K. (1999). Profile of the effective vocational agriculture teacher. Journal of Agricultural Education, 30 (2), 33-40. Mumford, M. (2003). Where have we been, where are we going? Taking stock in creativity research. Creativity Research Journal, 15 (2), 107-120. Newcomb, L.H., McCracken, J.D., & Warmbrod, J.R. (1993). Danville, Illinois: Interstate Publishers. Oliver, J., & Hinkle, D. (1982). Occupational educational research: Selecting statistical procedures. Journal of Studies in Technical Careers, 4 (3), 199 - 207. Perkins, D. (1988). The possibility of invention. In R. S. (Ed.), The nature of creativity (pp. 362385). New York: Cambridge University Press. Renzulli, J. (1992). A general theory for the development of creative productuvuty through the pursuit of ideal acts of learning. Gifted Child Quarterly, 36, 170-182. Rosenshine, B. & Furst, N. (1971). Research on teacher performance criteria. In O. S. (Ed.), Research in Teacher Education (pp. 37-72). Englewood Cliffs, NJ: Prentice Hall. Runco, M. A. (1997). The Creativity research handbook (Vol. 1). Cresskill, New Jersey: Hampton Press. 137

Salant, P. & Dillman, D.A. (1994). How to conduct your own survey: Leading professionals give you proven techniques for getting reliable results. New York: John Wiley. Starko, A. (2005). Creativity in the classroom: Schools of curious delight (third ed.). Mahwah, New Jersey: Lawrence Erlbaum Associates. Sternberg, R. (1999). Handbook of creativity. (R. Sternberg, Ed.) New York: Cambridge University Press. Sternberg, R. (2006). The nature of creativity. Creativity Research Journal, 18 (1), 87-98. Torrance, E. (1981). Creative teaching makes a difference. In J. K. J.C. Gowan, Creativity: Its educational implications (2nd ed., pp. 99-108). Dubuque, IA: Kendall/Hunt. Torrance, E. (1995). Why fly? A philosophy of creativity. Norwood, New Jersey: Ablex Publishing Corporation.

Authors Mollie S. Aschenbrener is an instructor in the College of Agriculture at California State University, Chico, 125 Plumas Hall, Chico, CA 95928. Email: [email protected]. Phone: (530) 898-4566. Rob Terry is a professor in the department of agriculture education at the University of Missouri, 127 Gentry Hall, Columbia, MO 65211. Email: [email protected]. Phone: (573) 884-7375. Fax: (573) 884-4444. Robert M. Torres is a professor in the department of agricultural education at the University of Missouri, 126 Gentry Hall, Columbia, MO 65211. Email: [email protected]. Phone: (573) 884-7376. Fax: (573) 884-4444.

138

FACULTY KNOWLEDGE AND PERCEPTIONS OF THE SCHOLARSHIP OF TEACHING AND LEARNING Lucas D. Maxwell, University of Missouri Anna L. Ball, University of Missouri Tracy Irani, University of Florida Abstract The purpose of this study was to determine faculty members’ knowledge and perceptions of the Scholarship of Teaching and Learning (SoTL) at a large land-grant university. A survey of faculty with appointments in specific applied sciences colleges and institutes at a large landgrant university served as the population for this study. The objectives of the study were to describe characteristics of faculty in regard to teaching, determine knowledge of the definition of the SoTL, describe faculty involvement in the SoTL, and determine faculty members’ perceptions about the value of and attitudes toward the SoTL. Nearly one-third of respondents were not familiar with the SoTL. More than eighty percent indicated that they had never, on their own or through collaboration, completed research about the SoTL. Almost sixty percent of respondents strongly agreed or agreed that SoTL is a valid form of scholarship, yet less than one-third of respondents felt that conducting research in the SoTL would be useful to their tenure and promotion dossier. In general, most faculty were neutral or positive in regards to the SoTL with almost two-thirds indicating they would like to learn more about the topic. Introduction/ Theoretical Framework In many classrooms across the nation, teaching occurs behind closed doors. The act of and products of teaching have remained a sole endeavor among the students and the instructor. Unlike traditional forms of scholarship, teaching as a scholarly pursuit is rarely based upon an intellectual inquiry, subject to peer review, and made available to a broader public. Thus, many universities across the nation have been reluctant to accept teaching as a valid form of scholarship (Shulman, 1993). Since the introduction of the concept of the Scholarship of Teaching and Learning (SoTL) more than 15 years ago, the notion of teaching as a scholarly endeavor equal to more traditional forms of scholarship has been the topic of much debate (Witman & Richlin, 2007). The basic concepts surrounding the SoTL were originally proposed by Ernest Boyer and, over the years, have been further refined through many research articles and books over the past ten years (Kreber, 2005). The move toward documenting the SoTL has been driven by market demands and public concern over the quality of teaching in the classrooms and laboratories of American universities (Kreber, 2007). As such, much attention has been paid to the SoTL and slowly, faculty across disciplines are beginning to recognize its value (Witman & Richlin, 2007). Often, the SoTL means different things to different faculty members. When Boyer proposed the original concepts surrounding the SoTL he did not provide a definition, rather a set of characteristics that served as an outline (Defining SoTL Hand-out, 2008). The literature has indicated several working definitions of the SoTL, in addition to some disagreement in the SoTL communities of practice, regarding one single definition. In describing the SoTL Boyer (1990) stated that “As a scholarly 139

enterprise, teaching begins with what the teacher knows…Pedagogical procedures must be carefully planned, continuously examined, and relate directly to the subject taught” (Defining SoTL Hand-out, 2008). While this description describes scholarly teaching, it does not serve as the basis upon which most other definitions are regarded (Defining SoTL Hand-out, 2008). According to Lee Shulman (1999), a teaching act is scholarly when it is made available to the academic public, is critically reviewed and evaluated by an academic or teaching discipline, and when said discipline utilizes or develops new work as a result of it. Several variations of this definition exist today, however most center around notions of public availability, peer review, and contribution. For the purpose of this study the researchers adopted the definition used at Illinois State University (ISU) in Normal, Illinois and will define the SoTL as the systematic reflection on teaching and learning made public (Scholarship of Teaching and Learning, n.d.). The process described by Shulman (1999) is quite common when referring to one’s research activity and findings; however, teaching has often been considered a much more private enterprise (Herteis, 2006). As a more consumer-driven, business-model of education emerges, higher education faces increasing pressure from stakeholders regarding program quality. Not only is the value of the curriculum taught being questioned but teaching quality is coming under increasing scrutiny as well. As a result of a more consumer-driven, high stakes notion of American education, “SoTL is an imperative today and not a choice” (Huber & Hutchens, 2005). Disciplines attempt to adopt SoTL practices in different ways. Many faculty members do not engage in the SoTL because of “the absence of support and reward for doing so” (Witman & Richlin, 2007, p.4). While some disciplines have embraced efforts in the SoTL more than others, in general, there is still room for improvement. Ultimately, the reward for conducting work in the SoTL will come from the respective researchers discipline; therefore it is important that studies in and about the SoTL be conducted across disciplines (Witman & Richlin, 2007). By conducting the SoTL work a researcher is able to “explore how to create the vital connection between themselves and the ‘subject’, themselves and the students, and students and the ‘subject’” (Kreber, 2007, p. 3). Much of the current work being conducted on the SoTL has focused primarily in regard to the status of the SoTL movement itself. Witman and Richlin (2007), in an assessment of the status of the SoTL across different disciplines, found that they first had to address the differences between scholarly teaching and the scholarship of teaching and learning. They noted that while scholarly teaching and the scholarship of teaching and learning shared similar elements they differed in goals and in their final output (Witman & Richlin, 2007). The SoTL aims to “result in a formal, peer-reviewed communication in an appropriate medium, or venue, which then becomes part of the knowledge base” (Witman & Richlin, 2007, p.2). In contrast, scholarly teaching aims to impact teaching and learning in a classroom in the immediate sense (Witman & Richlin, 2007). Much variation between the disciplines studied was found both in how the SoTL is interpreted as well as how it is valued. Among the professions, and more specifically within higher education, it has been posited that the SoTL is slowly becoming more widespread. Yet, for many years the professions have focused on providing teaching tips to faculty members rather than rewarding scholarly work in the areas of teaching and learning (Witman & Richlin, 2007).

140

Kreber (2005) suggested several goals or focus areas be considered and applied to the SoTL. In particular SoTL work should be focused on defining the SoTL and “whom we see as practicing the scholarship of teaching” (Kreber, 2005, p. 402). Also, it has been suggested that practitioners broaden their focus and look at larger issues facing curriculum and the overall college mission rather than focusing simply on how students learn (Kreber, 2005). Traditionally, colleges of agriculture have prided themselves in being student centered and often home to the best teachers on campus. As a result, one would expect to find a high level of awareness of the SoTL and an equally high level of participation in the SoTL research. Unfortunately, data to support these claims does not exist, nor does an abundance of research exist on how faculty perceive the SoTL and/or conduct work in the scholarship of teaching and learning, either within colleges of agriculture or university-wide. In order to increase programming in the SoTL, make the results of teaching more public as opposed to an isolated event behind a closed classroom door, and create a sense of value for scholarship in teaching and learning as equal to scholarship in research, more research is needed regarding what faculty know about the SoTL, how they conduct work in the SoTL, and how they value the SoTL in specific colleges. Purpose/Objectives The purpose of this study was to determine faculty members’ knowledge and perceptions of the SoTL. The following objectives guided the stated purpose: 1. 2. 3. 4.

Describe faculty members’ rank and levels of experience at a large land grant university. Determine faculty members’ knowledge of the definition of the SoTL. Describe faculty members’ involvement in the SoTL work. Determine faculty members’ perceptions regarding the value of and attitudes toward the SoTL. Methods/Procedures

The purpose of this study was to determine faculty members’ knowledge and perceptions of the SoTL. This applied survey research was conducted in an entirely electronic format. Notices were sent via electronic mail to faculty in the College of Agricultural and Life Sciences at the University of Florida (UF) as well as faculty in the UF Emerging Pathogens, Genetics, and Water Multidisciplinary Institutes. The survey instrument was developed for use with the online service Survey Monkey. Email based surveys present unique challenges for some groups. According to Dillman (2007) Certain populations, such as university professors, federal government employees, workers in many companies and corporations, and members of some professional organizations, generally have Internet addresses and access. For these populations, e-mail and Web surveys may have only minor coverage problems (p. 356). Despite their access to internet, a recent study of faculty members showed an average response rate for email surveys of thirty-two percent compared to forth-seven percent for postal delivered surveys (Shannon & Bradshaw, 2002). Despite this lower rate the researcher chose to deliver the survey electronically, using multiple contacts, due to budgetary and time constraints. 141

Participants received a pre-notice email message informing them that they will soon be asked to complete a questionnaire (Dillman, 2007). Following the pre-notice email participants received an email message containing a cover letter explaining the study with a link directing them to the Survey Monkey™ website for the instrument. According to Dillman (2007) the email containing the actual link to complete the survey should follow about two to three days later. In total, participants were contacted four times. Studies have shown that when email surveys are used, a four contact strategy produces response rates similar to surveys conducted using the postal service delivered format (Dillman, 2007). A group of 855 faculty in the College of Agricultural and Life Sciences at the University of Florida (UF) as well as the UF Emerging Pathogens, Genetics, and Water Multidisciplinary Institutes served as the final population of this study. Lists containing faculty names and emails were obtained for each group. A census of the accessible population resulted in 287 questionnaires returned. A total of ninety recipients declined to participate in the study and an additional twelve were not reached due to invalid email addresses. This resulted in a final response rate of 38.1%. To control for non-response error, early and late responders were compared in regard to two select demographic variables. These comparisons were made on the assumption that those participants that respond later, often after additional requests for participation, are more like non-responders (Armstrong & Overton, 1977). After comparison, no significant differences existed between the groups therefore there was no reason to believe that non-respondents were different than respondents. Table 1 outlines participation and response rates in this study. Table 1: Response Rates Response Categories Total Responded Opted Out Invalid Email

Counts 855 287 90 12

The survey instrument was developed by the research team based upon a review of literature of similar knowledge and perception studies. Many questions were based on a previously developed instrument used at Illinois State University (ISU) in Normal, Illinois. The research team received written permission from the developer of the ISU instrument to use it as the basis of the instrument for the study. To establish face and content validity the instrument was reviewed by an expert panel of selected faculty in the researchers’ department, who were experts in survey design as well as the SoTL work. The reliability of the instrument was analyzed post-hoc, and the instrument yielded a Cronbach’s alpha coefficient score of .862. All data were collected and stored on Survey Monkey™ until participants were contacted using Dillman’s (2007) four contact method and been given ample opportunity to respond. Data was then transferred and analyzed using the Statistical Package for the Social Sciences (SPSS). Standard statistical measures were preformed to describe the results and determine relationships between variables. Descriptive statistics including frequencies and percentages were determined and used to describe the respondents’ perceptions. Open-ended questions were coded for themes using a basic domain analysis. Recurring themes were identified in the open-ended questions 142

within the survey, and were coded by hand using highlighter markers. An audit trail, a reflexive journal, and peer debriefing was utilized in order to maintain trustworthiness and credibility of the qualitative data analysis (Denzin & Lincoln, 2005) . Results/Findings The first objective of this study was to describe characteristics of the faculty population. The respondents had an average of 13.9 years teaching at the University level. Table 2 contains information regarding total years teaching for respondents. Frequencies and percentages of respondents were reported for each category. Table 2: Faculty Members’ Total Years of Teaching (n=216) Years Taught at University 0-5 6-10 11-15 16-20 21-25 26-30 31+ Other

f 58 44 28 23 25 18 17 3

% 26.8 20.3 12.9 10.6 11.5 8.3 7.7 1.3

Table 3 provides data regarding the position held by each of the respondents. Eightythree percent of respondents indicated they were tenured or tenure track professors. The remaining seventeen percent were lecturers, instructors and individuals holding research titles. Table 3: Faculty Members’ Departmental Rank (n=234) Position Held in Department Assistant Professor Associate Professor Full Professor Adjunct Faculty Member Assistant Instructor Lecturer Other

f 62 53 79 2 1 6 31

% 26.5 22.6 33.8 0.9 0.4 2.6 13.2

The second objective of this study was to determine faculty knowledge of the definition of the SoTL. In order to asses this objective participants were asked to provide their own personal definition of the SoTL. In addition they were asked to discuss the similarities and differences between the SoTL and other types of scholarship. In regard to the ways that faculty members described the SoTL through their own personal definitions, three themes emerged. Such themes included definitions explicitly stating that they didn’t know how to define the SoTL, definitions of the SoTL as an individual activity to improve teaching and learning, and finally, definitions of the SoTL as a comprehensive form of scholarship.

143

More than one-third of the respondents who were asked to define the Scholarship of Teaching and Learning wrote that they had never heard of the SoTL and thus could not define it. Comments such as, “I have not heard of the concept before now,” clearly indicated that a number of the individuals in the study, could not define the SoTL in absence of a researcher-developed definition. The second theme in regard to the ways in which faculty members defined the SoTL involved responses that defined the SoTL as about improving teaching and learning. Among these definitions, the SoTL was defined more as a process of trial and error undertaken on an individual basis rather than a systematic approach to evaluating teaching and learning and then sharing it through presentations and peered reviewed publications. A representative quote of “It is the use of certain teaching methods by professors that have been determined to be effective by research in the field of education” supported this theme. The third theme regarding the ways in which faculty members defined the SoTL included a small portion of respondents who provided an understanding of the SoTL as moving beyond teaching tips, investigating teaching in systematic and scholarly ways, and making the results of such investigations as well as the creative works products of teaching subject to peer review and available to a larger public. One respondent provided the following definition, “the process of developing research questions, collecting and analyzing data, making inferences and drawing conclusions, and publishing these results on or about teaching and learning.” Yet another stated that “SoTL is the study of process, methods, accomplishments (including, assessment of student learning) and the synthesis of this information to share with others in the form of publications, presentations, workshops, etc.” In addition to formulating a general definition for the SoTL, faculty members were also asked to describe the ways in which scholarship in teaching was similar to or different from more traditional forms of scholarship. The major theme that emerged from the data was the difference between what ought to occur and what actually occurs in faculty work. In general, respondents found many more similarities between SoTL and other types of scholarship than differences with one respondent stating simply that there “should be none if done well.” Statements similar to this were repeated several times but were often followed by qualifying statements such as “in an ideal world.” Responses such as these seem to indicate that while the SoTL may be technically no different than other forms of scholarship; it is often perceived as different. According to one respondent, the only differences lie in the “perception of academic peers.” The same respondent went on to state, “the overall feeling is that high SoTL does not grant tenure whereas research scholarship does.” Despite an overall sense that there is no real difference between the SoTL and other types of scholarship nearly one third of respondents again answered that they did not know enough about the SoTL to answer the question. After participants answered the above questions they were provided with a definition of the SoTL which they were to keep in mind as they completed the questionnaire. For the purpose of this study about the SoTL the researcher adopted the definition used at Illinois State University and will define the SoTL as the systematic reflection on teaching and learning made public (Scholarship of teaching and Learning, n.d.). Based on this definition, Objective three was to describe the current level of faculty involvement in the SoTL. Participants were asked a series of questions regarding their 144

involvement with the SoTL. Table 4 outlines responses to each of four forced choice questions. In response to the first three questions, more than eighty-percent of respondents indicated that they had never conducted, collaborated, or published SoTL research. Nearly seven percent of respondents indicated some other form of participation in the SoTL. Faculty that indicated being involved in the SoTL in some other way most often listed serving in some capacity as a journal editor or reviewer. The majority of this involvement dealt with regional and national journals in their respective disciplines. There was some mention of grants that had been received to fund projects relating to the SoTL. However, most faculty involvement seemed geared toward the review of others work as opposed to generation of their own work in the SoTL. Table 4: Faculty Members’ Involvement in the SoTL

f(%)

Question 1. Have you conducted or been involved in SoTL research? 2. Have you collaborated with colleagues on SoTL research? 3. Have you ever published SoTL research? 4. Is there any other way you are involved in SoTL research?

Yes 37(17.9) 38(18.4) 33(16.1) 14(6.9)

No 170(82.1) 168(81.6) 172(83.9) 190(93.1)

n 207 206 205 204

For Objective four, participants were asked to respond to a series of questions and statements to determine their perceptions about the value of and attitudes toward the SoTL. Table 5 presents faculty responses when asked what type of impact, if any, does or would conducting the SoTL have on your professional career? While fifty percent responded neutral to the question, more than forty-four percent indicated that conducting work in and about the SoTL would have a positive or very positive impact on their professional careers. Table 5: Impact of the SoTL on Professional Career (n=178) f(%) Question VP P Neutral What impact does or would conducting SoTL research have on your 12(6.7) 67(37.6) 89(50) professional career? (VP=Very Positive, P= Positive, N=Negative, VN=Very Negative)

N

VN

10(5.6)

0(0)

Next, participants were asked where published or presented SoTL research would “count” in their annual department or unit evaluations for purposes of promotion and tenure. Table 6 contains participant responses. Nearly forty-five percent of respondents indicated that presenting or publishing SoTL research would count in the teaching area of their annual evaluation. Of the remaining responses, less that thirty percent indicated that SoTL work would count in the area of Scholarship/Research. Table 6: The SoTL Status in Departmental Evaluation (n=150) f(%) Service/ Scholarship/ I would have Question Teaching Extension Research a choice If you present or publish SoTL research, where would it “count” in 18(12.0) 66(44.0) 43(28.7) 23(15.3) your departmental annual evaluation?

145

Finally, participants were asked to indicate their level of agreement with statements regarding their motivation for and value of the SoTL. Table 7 shows response frequencies and percentages for each of the statements. Table 7: Faculty Perceptions of the Value of SoTL (n=175) f(%) Statement SA A NA/D D SD 1. SoTL has practical value for 44(25.6) 82(47.7) 43(25.0) 2(1.2) 1(.6) teachers. 2. SoTL has practical value for 43(25.3) 81(47.6) 43(25.3) 1(.6) 2(1.2) institutions of higher education. 3. SoTL is important. 40(23.3) 82(47.7) 47(27.3) 2(1.2) 1(.6) 4. SoTL has practical value for 35(20.3) 87(50.6) 47(27.3) 2(1.2) 1(.6) students. 5. Participation in SoTL research 31(18.1) 67(39.2) 66(38.6) 4(2.3) 3(1.8) would make me a better teacher. 6. SoTL is a form of “real” 29(17.0) 72(42.1) 63(36.8) 6(3.5) 1(.06) scholarship. 7. Participating in SoTL research would be personally rewarding to 27(15.9) 60(35.3) 70(41.2) 10(5.9) 3(1.8) me as a faculty member. 8. SoTL would take away time from my other responsibilities as a 26(15.1) 69(40.1) 55(32.0) 18(10.5) 4(2.3) faculty member. 9. SoTL has practical value for the 25(14.5) 61(35.5) 80(46.5) 4(2.3) 2(1.2) community. 10. I would like to learn more about 21(12.1) 84(48.6) 48(27.7) 15(8.7) 5(2.9) SoTL. 11. Knowing SoTL research in ones discipline is important for good 19(11.2) 74(43.8) 66(39.1) 6(3.6) 4(2.4) teaching. 12. Everyone should do some SoTL 10(5.8) 40(23.4) 82(48.0) 28(16.4) 611(.4) research. 13. SoTL is valued in my College. 9(5.2) 42(24.4) 87(50.6) 27(15.7) 7(4.1) 14. SoTL is valued in my Department. 9(5.3) 31(18.1) 87(50.9) 35(20.5) 9(5.3) 15. I am not interested in participating 8(4.7) 16(9.4) 66(38.8) 55(32.4) 25(14.7) in SoTL research. 16. SoTL would be useful to my 7(4.1) 39(23.1) 85(50.3) 22(13.0) 16(9.5) tenure and promotion dossier. 17. SoTL is valued in my University. 4(2.3) 33(19.2) 95(55.2) 32(18.6) 8(4.7) 18. Results from SoTL research are 4(2.4) 33(19.6) 91(54.2) 24(14.3) 16(9.5) used/applied in my department. 19.There is adequate funding for 3(1.8) 12(7.1) 98(57.6) 48(28.2) 9(5.3) SoTL. (SA=Strongly Agree, A=Agree, NA/D=Neither Agree nor Disagree, D=Disagree, SD=Strongly Disagree)

146

Nearly seventy-five percent of respondents strongly agreed or agreed that the SoTL was important and had practical value for teachers, students, and institutions of higher education. Respondents were evenly split when asked if the SoTL was valued in their department, college, and university. About fifty percent were neutral in regard to these statements with roughly twenty to twenty-five percent of the remaining respondents either agreeing or disagreeing. Almost sixty percent of respondents strongly agreed or agreed that SoTL is a “real” form of scholarship and participating in the SoTL research would make them a better teacher. Despite this less than one-third of respondents felt that conducting research in the SoTL would be useful to their tenure and promotion dossier. Conclusions/Recommendations/Implications Objective one of this study was to describe selected characteristics of the sample, in regard to rank and years in the profession. Respondents represented faculty at various departmental ranks and years of service. Comparisons of these groups indicated no significant differences based on demographics, suggesting that study participants are representative of the faculty population. It might be intuitive to assume that faculty members of higher ranks and/or more years toward tenure would know more about the SoTL, be more supportive of the SoTL, and be more engaged in conducting work in the SoTL. The results of this study were unable to support that anecdotal claim. Thus, faculty in general are largely unengaged in, and unaware of the SoTL, and it is recommended that faculty development programming in the SoTL be inclusive of and responsive to the needs of faculty members across rank and years of service. The goal of objective two was to determine faculty knowledge of the definition of the SoTL. After reviewing responses, three themes emerged from the data. Due to the fact that, more than one third of respondents indicated that they were not familiar with or had never heard of the SoTL and only a small minority of faculty members could provide an accurate, in depth definition of the SoTL, it was concluded that faculty are limited in their knowledge of and exposure to the SoTL. While not surprising, it is somewhat unfortunate that more than 15 years after the call to action regarding efforts toward creating a more comprehensive model of scholarship that includes scholarship in and of teaching, still more than one-third of faculty are not aware of its existence or meaning. The implication of this finding is that Boyer (1990) was well ahead of his time, almost twenty years ago, when he suggested a model that moved well beyond the scholarship of discovery as the only valid form of faculty work. In addition to faculty members’ inability to form a definition of the SoTL, a group of respondents who were familiar with the term provided definitions that were more consistent with the idea of scholarly teaching. Thus, it was concluded that some faculty members do not make a clear distinction between scholarly teaching and the scholarship of teaching and learning, whereas, this distinction is clearly documented in the SoTL literature (Richlin, 2001). While scholarly teaching and the SoTL certainly have similarities they ultimately have different goals. The goal of scholarly teaching being an immediate impact on teaching and learning in a classroom while the latter results in peer reviewed work that ultimately adds to the knowledge base (Whitman & Richlin, 2007). The implication of this finding is that perhaps faculty development efforts aimed at providing teaching tips to faculty members and helping them become more versed in the teaching and learning literature, while valid in their own right, do not 147

help faculty make a distinction between how to be a more scholarly teacher and ways to systematically investigate the inputs, process, and outcomes of teaching and learning. It is recommended that research be conducted on how faculty members learn to teach, the ways in which they become scholarly teachers, as well as how they conduct the scholarship of teaching and learning. It was clear from the results of Objective two that more research and faculty development efforts are needed to increase awareness about the SoTL. Previous studies have indicated a wide range in the levels of acceptance of the SoTL across disciplines and this study seems to support that research (Witman & Richlin, 2007). It would appear that the opportunity exists to build support for the SoTL through in-service programming for faculty, perhaps utilizing those individuals who have a record of producing SoTL research. Given that more information is needed on how faculty members think about their work in regard to the SoTL, it is recommended that more in depth qualitative research be conducted to produce grounded theory on faculty work in the SoTL as well as to provide more information for the design of future quantitative instruments. In addition to forming a definition of the SoTL, a more comprehensive description of faculty members’ knowledge of the SoTL was gleaned by asking faculty to describe the similarities and differences between scholarship in research, or more traditional forms of scholarship, and scholarship in teaching. It was concluded by the ways in which faculty members described such distinctions, that the perceptions by faculty members of what should be and the perceptions of the reality of their professional expectations are different. Qualifying statements made by faculty members such as “in an ideal world”, support this conclusion. Faculty members’ feel that the SoTL may be technically no different than other forms of scholarship but it is often perceived as different. The implication of this finding is that for scholarly work in teaching to become more prevalent, it must carry equal weight in the minds of faculty members in regard to promotion and tenure expectations. It is recommended that research be conducted regarding department chairs, deans, and other university level administrators knowledge and perceptions of the SoTL. In addition, future studies should focus on how the SoTL is perceived by tenure and promotion committees across disciplines. There appear to be some contradictions between responses regarding this issue. Responses to other questions indicated that a majority of faculty perceived that the SoTL is a “real” form of scholarship yet less than one-third indicated it would be useful to their tenure and promotion dossier. Further study on this issue will help to refine faculty perceptions about the SoTL and provide insight into what could be done to increase the perceived value of conducting and publishing SoTL research. Objective three sought to determine faculty involvement in the SoTL. Based upon the results, it was concluded that faculty members by and large were not involved in SoTL work, and those who described involvement in the SoTL, indicated that the nature of their involvement was to review teaching related articles within disciplinary journals. This finding implies that while faculty members described the SoTL as important, they are not involved in the SoTL work for some reason. Perhaps, faculty are not involved due to lack of awareness about the kinds of work they might conduct as a part of the SoTL, they feel that SoTL work is not a part of their 148

expectations, or they are not supported to conduct SoTL work in terms of funding or through administrator approval. Universities should provide faculty development programming efforts for faculty to learn about opportunity areas in the SoTL. More recognition opportunities as well as resources and support for faculty who are actively engaged in the SoTL should be created and given at the college, university, and national levels. Objective four was to determine faculty perceptions about the value of and attitude towards the SoTL. Based upon the findings, it was concluded that faculty perceived the potential for SoTL work in a positive light, however, with fifty percent of respondents indicating a neutral opinion regarding the value of the SoTL, it is suggested that further research be conducted regarding faculty members perceived motivation for and task value of conducting work in the SoTL. The need for education about the topic is evident based on responses to questions in objective two of this study. Quality in-service opportunities should be well received by faculty with more than sixty percent indicating they would like to learn more about the SoTL. A wide range of responses were received when asked where the SoTL research would count in a departmental evaluation. Despite indicating that the SoTL was a “real” form of scholarship, less than one-third of faculty members indicated that they would receive credit for the SoTL in the area of Scholarship/Research, and nearly half of faculty members indicated that SoTL work would count under the Teaching category. Thus, it was concluded that there is a discrepancy in how faculty characterize the nature of scholarly work in teaching. This seems to indicate the need for more uniformity in the area of evaluation as well as promotion and tenure. Future studies should focus on the promotion and tenure process and determine similarities and differences across disciplines as well has indentifying what criteria are used to determine if a work is considered scholarly. It was further concluded from the results of objective four that faculty members viewed the SoTL work to have practical value for teaching within their disciplines. This finding further implies that there could be a potential to expand faculty work in the SoTL through their willingness to improve teaching and to document the results of such teaching improvements in systematic ways. While improving learning is the ultimate goal of the SoTL, and the previous finding is a positive one, the finding further implies, that faculty members do not make clear distinctions between scholarly teaching and the scholarship of teaching and learning. Faculty development efforts should focus on providing faculty members teaching tips and ways to be come a more scholarly teacher, as well as assisting them in documenting teaching in systematic and scholarly ways, and delineating the differences between the two. The final conclusion in regard to faculty members’ perceptions of and attitudes toward the value of SoTL work was that faculty members are neither positive nor negative in regard to their view of the SoTL as personally rewarding. This finding implies that more value and respect for the SoTL is needed at all levels. Future efforts should focus on developing clear guidelines for how SoTL research can help faculty members experience personal growth as teachers and feel a sense of accomplishment. Additional efforts should be made to recognize and reward faculty who are actively engaged in the SoTL.

149

This descriptive study was undertaken to provide baseline data for future studies on the Scholarship of Teaching and Learning. The results, while descriptive in nature and limited to this specific population seem to provide ample areas for future study. They also seem to indicate the need for faculty development initiatives focused of building awareness about the SoTL. Additional study regarding faculty roles and responsibilities may be warranted with more than fifty-five percent of respondents indicating that participating in the SoTL research would take time from their other responsibilities as a faculty member. It is clear that much more work is needed to accurately determine the status of the SOTL across disciplines and universities. Previous studies have indicated, and these results seem to support findings indicating a very wide range of acceptance and participations in the SoTL (Whitman & Richlin, 2007). It is hoped that this baseline data will serve as a springboard for future studies about the Scholarship of Teaching and Learning. References Armstrong, J.S., & Overton, T.S. (1977). Estimating nonresponse bias in mail surveys. Journal of Marketing Research, 14(3), 10-36. Boyer, E. L. (1990).Scholarship reconsidered: Priorities of the professorate. San Francisco, CA: Jossey-Bass. Defining SoTL Hand-out (2008). Illinois State University, Retrieved February 27, 2008, from http://www.sotl.ilstu.edu/downloads/pdf/definingSoTL.pdf Denzin, N.K. & Lincoln, Y.S. (2005). 3rd Ed., The handbook of qualitative research. Thousand Oaks: CA, Sage Publications. Dillman, D. (2007). Mail and Internet Surveys: The Tailored Design Method (2nd ed.). New York:Wiley. Herteis, E. M. (2006). What’s the Evidence?, Tapestry number 13, Retrieved February 19, 2008, from http://www.tag.ubc.ca/resources/tapestry/archive/06/Number13/what'sevidence.pdf. Huber, M. T., & Hutchings, P. (2005). The advancement of learning: Building the teaching commons. San Francisco, CA: Jossey-Bass. Kreber, C. (2005). Charting a critical course on the scholarship of university teaching movement, Studies in Higher Education 30(4), 389-407. Kreber, C. (2007). What’s it really all about? The scholarship of teaching and learning as an authentic practice, International Journal for the Scholarship of Teaching and Learning 1(1), www.georgiasouthern.edu/ijsotl/v1n1/essays/kreber/IJ_Kreber.pdf. Richlin, L. (2001). Scholarly teaching and the scholarship of teaching. In Kreber, C. (ed.), scholarship revisited: Perspectives on the scholarship of teaching. New Directions for Teaching and Learning, no.86. San Francisco, Jossey-Bass. 150

Scholarship of Teaching and Learning (n.d.). Illinois State University, Retrieved March 27, 2001, from http://www.sotl.ilstu.edu/ Shannon, D. M. & Bradshaw. C. C. (2002) A comparison of response rate, response Time, and costs of mail and electronic surveys. Journal of Experimental Education, 70(1), 179-192. Shulman, L. (1993, November) Teaching as community property: Putting an end to pedagogical solitude, Change 6(7). Shulman, L. (1999). Taking learning seriously, Change 31(4). Witman, P. D. & Richlin, L. (2007). The status of the scholarship of teaching and learning in the disciplines, International Journal for the Scholarship of Teaching and Learning 1(1), http://www.georgiasouthern.edu/ijsotl/v1n1/essays/witman/IJ_witman.pdf. Authors Lucas D. Maxwell is a PhD Graduate Student in the Department of Agricultural Education at the University of Missouri, 124 Gentry Hall, Columbia, MO 65211. Email: [email protected]. Phone: 573-884-7561. Fax: 573-884-4444. Anna L. Ball is an Associate Professor in the Department of Agricultural Education at the University of Missouri, 122 Gentry Hall, Columbia, MO 65211. Email: [email protected]. Phone 573-884-9797. Fax: 573-884-4444. Tracy Irani is an Associate Professor in the Department of Agricultural Education and Communication at the University of Florida, 220 Rolfs Hall, Gainesville, FL 32611. Email: [email protected]. Phone: 352-392-0502. Fax: 352-392-9585.

151

THE EFFECT OF AN INTEGRATED COURSE CLUSTER LEARNING COMMUNITY ON THE ORAL AND WRITTEN COMMUNICATION SKILLS AND TECHNICAL CONTENT KNOWLEDGE OF UPPER-LEVEL COLLEGE OF AGRICULTURE STUDENTS Cynthia Barnett, University of California Greg Miller, Iowa State University Thomas A. Polito, Iowa State University Lance Gibson, Pioneer Hi-Bred International, Inc. Abstract The purpose of this quasi-experimental study was to determine if upper-level college students who participated in AgPAQ, an integrated course cluster learning community, would demonstrate enhanced learning in the areas of oral communication, written communication, and agronomic/economic technical content knowledge. The population (N=182) consisted of students who participated in AgPAQ, and five comparison groups: students in a farm management class; students in a stand-alone soil, fertilizer, and water management class; students in a soil, fertilizer, and water management class linked with an English course; and students in a paid volunteer group who had not previously participated in AgPAQ. Instruments included three rubrics that measured performance on written communication, oral communication, and agronomic/economic technical content knowledge. Analyses revealed that AgPAQ participants scored higher than non-AgPAQ participants on measures of oral and written communication in all comparisons. Also, AgPAQ participants scored higher on measures of agronomic/economic technical content knowledge than students in the non-AgPAQ paid volunteer group and students in the stand-alone soil, fertilizer, and water management class. AgPAQ participants also scored higher, but not significantly higher, than students in an English and agronomy linked integration. AgPAQ fostered enhanced learning in oral communication, written communication, and agronomic/economic technical content knowledge. Introduction In the past, college and university curricula focused on delivering information to students through lectures and other pedagogies that involved little or no social interaction on the part of the students. Though lectures and other didactic strategies still dominate many college courses, highly structured, rote learning pedagogy does not appropriately take into account the individual experiences and goals students bring to classrooms and lecture halls. The concept that learners bring prior knowledge and experiences to learning environments is the basis of educational philosopher John Dewey’s (1933, 1938) notion of “development from within” (Dewey, 1938, p. 1), the idea that education is meaningful when it includes interaction between the learner’s prior knowledge and experience and what is being learned. Dewey and others (Cremin, 1962; Ravitch, 1983; Zilversmit, 1993) proposed progressive education— education that encourages integrated understanding through unrestricted investigation. Some contemporary pedagogy now offers progressive learning experiences that privilege experience over rote learning, interaction over silence, applied learning over isolated experimentation and lecture, and courses that integrate rather than isolate the academic disciplines to make learning more meaningful. 152

Higher education should provide opportunities for students to actively use as well as formally demonstrate the knowledge and skills they learn in their courses (Boyer Commission on Educating Undergraduates in the Research University [Boyer Commission], 1998; Kolb, 1984; Taylor, Moore, MacGregor, & Lindblad, 2003). Parents and employers join faculty and administrators in calling for a higher education environment that effectively challenges students and better prepares them for the rapidly changing world (Smith, MacGregor, Matthews, and Gabelnick, 2004). For employers to keep up with the quickly changing nature of the workplace, they need employees to come to them directly from colleges and universities ready to use their knowledge and skills (Secretary’s Commission on Achieving Necessary Skills [SCANS], 1991). In the context of such change and compounded by stiff competition within the worldwide employment market, employers demand a high level of competence. They expect recent graduates to combine information with practical experience (SCANS). Major agricultural employers recruit and seek employees who have experience and are accomplished at teamwork, critical thinking, problem-solving, and oral and written communication skills (Boyer Commission, 1998). Colleges of agriculture must offer courses that effectively teach these skills. Theoretical Framework In 1984, Kolb asserted that experience provides “the foundation for an approach to education and learning as a lifelong process that is soundly based in intellectual traditions of social psychology, philosophy, and cognitive psychology” (p. 3–4). Simply put, experiential learning can help students “achieve higher levels of thought and retain information longer than students who work quietly as individuals” (Gokhale, 1995, p. 22). Kolb (1984) defines experiential learning as a means “for examining and strengthening the critical linkages among education, work, and personal development” (p. 4). Learning takes place when an individual reflects on a direct experience. Next, they generalize how what they have learned may apply to other situations. Finally, they apply this learning through additional related experiences. Cove and Love (1996) observed that higher education has struggled with “increasing fragmentation of the learning process, disciplines and knowledge, administrative structure, and community” (p. 2). The learning community concept developed in response to this fragmentation and it provides a means of implementing experiential learning theory. Learning communities are “a variety of curricular approaches that intentionally link or cluster two or more courses, often around an interdisciplinary theme or problem, and enroll a common cohort of students” (Smith et al., 2004, p. 20). Learning community scholars have identified five major models. Models relevant to this study are the linked courses model and the integrated course clusters model (Gabelnick, MacGregor, Matthews, & Smith, 1990).

153

Linked courses are two courses—perhaps from different departments—that are connected such a skills building class (e.g., a writing course) and a class that is more discipline specific (e.g., an agronomics course). In this model, faculty members meet frequently as a team before and during the semester to coordinate syllabi, develop joint assignments, and plan activities focused on the learning community’s common educational goals (Gabelnick et al., 1990). Integrated course clusters are an “expanded form of the linked course model” (Gabelnick et al., 1990, p. 21) in which three or four separate courses are linked by “common themes, historical periods, issues or problems” (Gabelnick et al., p. 32) and are scheduled together to form a “cluster.” A learning community course cluster is usually composed of students who register for the learning community, meaning that an integrated course cluster may comprise the entire course load for those students. Although scholarship about learning communities has proliferated in the past decade, most of that research has focused on learning community models that do not involve agricultural courses. In several cases, the design of learning communities has included a writing course linked to other discipline-specific courses such as engineering, medicine, history, or the humanities (Taylor et al., 2003; Tinto, 2000). Because of past research, there is reason to believe that learning communities can positively affect student learning of technical content (Hanson and Rawlinson, 2003; Lichtenstein, 2005; Seels, Campbell, and Talsma, 2003; Smith and Bath, 2006; Sterba-Boatwright, 2000; Zhao and Kuh, 2004), oral communication skills (Cowen, 2000; Cyphert, 2002; Thompson, 1990), and written communication skills (Cowen; Cyphert; Lichtenstein; Thompson). These are high-priority outcomes for agricultural employers. Even so, no studies have been conducted on integrated course cluster learning communities in agriculture. We do not know whether students who participate in agricultural learning communities develop improved technical content knowledge, oral communication skills, and written communication skills. Purpose and Hypotheses The purpose of this study was to determine whether students who participated in an integrated four-course-cluster agriculture-related learning community demonstrated enhanced learning in oral communication, written communication, and agronomic/economic technical content knowledge compared with students who did not participate in the integrated four-coursecluster agriculture-related learning community. This quasi-experimental study was guided by the following research hypotheses: 1.

Students who participated in the integrated four-course-cluster agriculture-related learning community will attain higher scores on a measure of oral communication skills than students who participated in an agricultural capstone farm management course.

2.

Students who participated in the integrated four-course-cluster agriculture-related learning community will attain higher scores on a measure of written communication skills than students who participated in an agricultural capstone farm management course.

154

3.

Students who participated in the integrated four-course-cluster agriculture-related learning community will attain higher scores in the area of written communication skills compared with students who participated in a stand-alone soil, fertilizer, and water management course, an English and agronomy linked integration, and a self-selected paid volunteer group of agriculture students who did not participate in the integrated four-course cluster agriculture-related learning community.

4.

Students who participated in the integrated four-course-cluster agriculture-related learning community will attain higher scores in the area of agronomic/economic technical content knowledge compared with students who participated in a stand-alone soil, fertilizer, and water management course, an English and agronomy linked integration, and a selfselected paid volunteer group of agriculture students who did not participate in the integrated four-course-cluster agriculture-related learning community.

5.

A self-selected paid group of past participants from the integrated four-course-cluster agriculture-related learning community will attain higher written communication scores and agronomic/economic technical content knowledge scores when solving a multidisciplinary problem compared with a self-selected paid volunteer group of agriculture students who did not participate in the integrated four-course-cluster agriculture-related learning community. Procedures

Design Two of Campbell and Stanley’s (1963) research designs were used in this quasiexperimental study. The Nonequivalent Control Group Design was used to test hypotheses one and two. A Modified Static-Group Comparison Design was used to test hypotheses three and four. In the modified static group comparison design, neither treatments nor dependent variable measures were administered concurrently across comparison groups. The Static-Group Comparison Design was used to test hypothesis five. Population The target population was junior and senior undergraduate students in the College of Agriculture at Iowa State University. The accessible population (N = 182) consisted of all students who participated in the integrated four-course-cluster agriculture-related learning community during the fall semesters of 2004 and 2005 (n = 33) and students from the following comparison groups: an agricultural capstone farm management course during the fall semesters of 2004 and 2005 (n = 57); a stand-alone soil, fertilizer, and water management course during the fall semesters of 1996, 1997, and 2003 (n = 36); and an English course integrated and linked with a soil, fertilizer, and water management course during the fall semesters of 1999, 2000, and 2002 (n = 35). To test hypothesis five, a self-selected paid group of past participants from the integrated four-course-cluster agriculture-related learning community (n = 7) and a self-selected paid volunteer group of students who did not participate in the integrated four-course-cluster agriculture-related learning community (n = 14) were used. Comparison groups were chosen 155

based on their shared emphasis on enhancing communication skills and real-world problem solving skills. Experimental Group The integrated four-course-cluster agriculture-related learning community was named AgPAQ (Agriculture students Providing integrated solutions to Agronomy and farm business management Questions) and was developed for junior and senior students. AgPAQ was initiated in the fall of 2004 at Iowa State University. AgPAQ integrated an English class, an agricultural economics class, and two agronomy classes. AgPAQ’s mission was to integrate knowledge and skills from each of the linked courses to enable students to successfully solve professional, work-based, agriculture problems. A major aspect of the AgPAQ learning community was the consultant relationship students developed while identifying problems and opportunities and recommending improvements for a local farmer. Comparison Groups Students in the farm management capstone classes participated in the management and operation of a diversified farm. This required them to perform decision making responsibilities needed for planning, record keeping, and buying and selling the farm's livestock, crops, and equipment. Farm management capstone students carried out team activities similar to the multidisciplinary integration activities performed by AgPAQ team members. The farm management capstone course was not formally linked to or integrated with any other course. Variables measured in this group as a comparison to the AgPAQ group were written communication and oral communication. Data were collected from committee reports generated at the beginning of each semester and state-of-the-farm reports generated by the same teams at the end of each semester. In the Agronomy 356 course students learned basic principles related to tillage, soil drainage, soil erosion and erosion control, soil fertility, and nutrient application while making management recommendations that directly affected economic viability and environmental sustainability for a farmer client. These students worked in teams that participated in activities similar to the multidisciplinary integration activities performed by AgPAQ team members. In 1996, 1997, and 2003, Agronomy 356 was not formally linked to or integrated with any other course. Agronomy 356 and English 309 were linked and integrated in 1999, 2000, and 2002. English 309 covered the theory and practice of writing reports and proposals. Agronomy 356 students learned basic principles related to tillage, soil drainage, soil erosion and erosion control, soil fertility, and nutrient application while making management recommendations directly affected on economic viability and environmental sustainability for a farmer client. These students worked in teams that participated in activities similar to the multidisciplinary integration activities performed by AgPAQ team members.

156

In 2005 and 2006, members of the paid AgPAQ volunteer comparison group were recruited by AgPAQ instructors. An invitation was offered to all students who had previously participated in AgPAQ. Past AgPAQ students who became part of this group addressed a professional, work-related multidisciplinary problem similar to the problem they had addressed in AgPAQ. Students worked in teams 12 hours per week for 6 weeks and were paid $500 each. The paid non-AgPAQ volunteer comparison group consisted of two groups of students who did not participate in AgPAQ and were not associated with any courses in the integration. Students were recruited from within the College of Agriculture at Iowa State University. The volunteers were randomly assigned to work teams to address a set of real multidisciplinary problems similar to the problems addressed by the paid AgPAQ volunteer group. Non-AgPAQ students worked 12 hours per week for 6 weeks and were paid $500 each. For the Agronomy 356, agronomy/English linked course, AgPAQ volunteer, and nonAgPAQ volunteer groups, variables measured as a comparison to the AgPAQ groups were written communication and agronomic/economic technical content and data sources were the client recommendation reports generated by students at the end of the semester or work period. Instrumentation Pretest and post-test instruments used in this study included three rubrics that measured performance on written communication, oral communication, and agronomic/economic technical content knowledge. A 4-point, Likert-type scale was used for scoring each rubric. Each level was given a numeric value for statistical analysis: 3 = exemplary, 2 = proficient, 1 = marginal, and 0 = unacceptable. Face and content validity for each rubric—written, oral, and agronomic/ economic—was established by a panel of experts within each area. Each panel performed a tworound evaluation to verify that each instrument contained the correct criteria to accurately measure elements of written and oral communication as well as agronomic/ economic technical knowledge. At the conclusion of the second round of evaluation, 80% (n = 4) of the experts determined the written communication tool was face and content valid, 100% (n = 5) of the experts determined the oral communication rubric was face and content valid, and 100% (n = 5) of the experts determined the agronomic/economic technical knowledge rubric was face and content valid. The written communication rubric had five criteria: content, development, organization, sentence structure (grammar, spelling, and mechanics), and style (voice, tone, and word choice). A panel of experts (n = 9) used the written communication rubric to score the written communication pieces. Each member of the panel worked individually on a random sample of the pieces. After 2 weeks, the same experts individually scored the same written communication pieces using the same rubric. The two sets of scores were correlated. The intrarater reliability coefficient was .83. To determine interrater reliability, two different groups of raters also scored the reports. Scores from group one were correlated with scores from group two. The correlation yielded a reliability coefficient of .28. First-round posttest data were used to assess internal consistency and yielded a Cronbach’s alpha coefficient of .92.

157

The oral communication rubric had six criteria: organization, style (verbal and non-verbal), content (depth and accuracy), oral language conventions (use of language and grammar and word choice), group interaction (responsiveness to audience and body language), and use of communication aids. A panel of experts (n = 15) used the oral communication rubric to score the oral communication pieces. Each member of the panel worked individually on a random sample of the pieces. After 2 weeks, the same experts individually scored the same oral communication pieces using the same rubric. The two sets of scores were correlated. The intrarater reliability coefficient was .89. To determine interrater reliability, two different groups of raters also scored the reports. Scores from group one were correlated with scores from group two. The correlation yielded a reliability coefficient of .46. First-round posttest data were used to assess internal consistency and yielded a Cronbach’s alpha coefficient of .90. The agronomic/economic technical content assessment rubric had 13 criteria: identification of problem and formulation of questions, conceptual framework, soil sampling, nutrient recommendations, drainage, soil conservation, geographic information system and mapping, crop management, analysis and interpretation of data gathered, farm records, budgets, and economic management recommendations. A panel of experts (n = 15) used the agronomic/economic rubric to score the recommendation reports. Each member of the panel worked individually on a random sample of the pieces. After 2 weeks, the same experts individually scored the same recommendation reports using the same rubric. The two sets of scores were correlated. The intrarater reliability coefficient was .75. To determine interrater reliability, two different groups of raters also scored the reports. Scores from group one were correlated with scores from group two. The correlation yielded a reliability coefficient of .78. First-round posttest data were used to assess internal consistency and yielded a Cronbach’s alpha coefficient of .88. Data Collection Professional communication experts—teachers, editors, industry specialists, and graduate students who were pursuing communication degrees—scored the reports individually using the oral communication and written communication rubrics. Professional agronomic/economic experts—professors and industry specialists—scored the recommendation reports using the technical content knowledge rubric. Each rater participated in a training session on how to score the reports using the appropriate rubric. At the conclusion of the training, each evaluator was given a packet that included randomly assigned reports and enough rubrics to score all of the pieces individually. Data Analysis Data analysis was performed using SPSS 14.0 for Windows. Data were collected, coded, and analyzed by the authors. Data analysis included frequencies, means, standard deviations, Pearson correlations, general linear models—ANOVA and ANCOVA, and the Tukey post hoc procedure. The alpha level was set a priori at .05. Results

158

Hypothesis 1 Analysis of covariance (ANCOVA) was used to adjust the AgPAQ and farm management comparison group oral communication posttest scores based on group differences observed on the pretest. The ANCOVA procedure revealed that the AgPAQ group had significantly higher adjusted posttest means (F = 54.75, p < .001, Table 1). To illustrate the magnitude of the difference, each adjusted posttest mean score was divided by the highest possible score on the rubric (18 points for the oral communication rubric). AgPAQ participants achieved posttest oral communication scores that were 31% higher than scores of the farm management comparison group. Table 1 AgPAQ/Farm Management Pretest/Posttest Oral Communication Mean Scores 95% Confidence Interval for Adjusted Posttest Means Groups AgPAQ Farm Management

Pretest Mean 14.88

Posttest Adjusted Mean 15.88

SE .53

Lower Bound 14.83

Upper Bound 16.93

9.59

10.27

.44

9.39

11.16

The data support the hypothesis that students who participated in the AgPAQ integrated course cluster would attain higher scores on a measure of oral communication skills than students who participated in the farm management comparison group. Hypothesis 2 Analysis of covariance (ANCOVA) was used to adjust the AgPAQ and farm management comparison group written communication posttest scores based on group differences observed on the pretest. The ANCOVA procedure revealed that the AgPAQ group had significantly higher adjusted posttest means (F = 93.32, p < .001, Table 2). To illustrate the magnitude of the difference, each adjusted posttest mean score was divided by the highest possible score on the rubric (15 points for the oral communication rubric). AgPAQ participants achieved posttest written communication scores that were 46% higher than scores of the farm management comparison group. The data support the hypothesis that students who participated in the AgPAQ integrated course cluster would attain higher scores on a measure of written communication skills than students who participated in the farm management comparison group.

159

Table 2 AgPAQ/Farm Management Pretest/posttest Written Communication Mean Scores 95% Confidence Interval for Adjusted Posttest Means Groups AgPAQ Farm Management

Pretest Mean 7.82

Posttest Adjusted Mean 12.69

SE .52

Lower Bound 11.66

Upper Bound 13.72

5.07

5.87

.44

4.98

6.75

Hypothesis 3 Table 3 shows means and standard deviations for written communication scores by group. The ANOVA procedure revealed there were significant differences between the groups’ written communication scores (F = 23.46, p < .001, one-tailed). The Tukey post hoc procedure revealed that the AgPAQ group mean score for written communication was significantly higher than scores of all other groups. Table 3 Written Communication Mean Scores by Group Group AgPAQ

M 12.52

SD 1.68

N 33

Soil, Fertilizer, Water Management

7.47

2.77

36

Agronomy 356/English 309

8.86

3.17

35

Paid Non-AgPAQ Volunteer Group

8.21

2.52

14

Results support the hypothesis that AgPAQ participants would attain higher scores on a measure of written communication skills than students who participated in a stand-alone soil, fertilizer, and water management course, an English and agronomy linked integration, and a selfselected paid volunteer group of agriculture students who did not participate in AgPAQ. Hypothesis 4 Table 4 shows means and standard deviations for the agronomic/economic technical content knowledge scores by group. The ANOVA procedure revealed there were significant differences between the groups’ agronomic/economic technical content knowledge scores (F = 12.94, p < .001). The Tukey post hoc procedure revealed that group mean differences between AgPAQ and the 356 stand-alone course as well as the paid non-AgPAQ volunteer group were significant. Results partially support the hypothesis that AgPAQ participants would attain higher scores on a measure of agronomic/economic technical content knowledge than students who 160

participated in a stand-alone soil, fertilizer, and water management course, an English and Agronomy linked integration, and a self-selected paid volunteer group of agriculture students who did not participate in AgPAQ. Table 4 Agronomic/Economic Technical Content Knowledge Mean Scores by Group Group M SD a AgPAQ 23.42 7.76

N 33

Soil, Fertilizer, Water Management

17.00b

5.04

36

Agronomy 356/English 309

21.86a

4.81

35

Paid Non-AgPAQ Volunteer Group 13.43b 6.81 Note. Means with different superscript letters are significantly different at p < .05.

14

Hypothesis 5 Table 5 shows that AgPAQ paid volunteer participants scored significantly higher on written communication and agronomic/economic technical content knowledge than a selfselected paid volunteer group of agriculture students who did not participate in AgPAQ. The research hypothesis was supported. Table 5 Written Communication and Technical Content Mean Scores by Group 95% Confidence Interval of the Difference Dependent Variable Written Communication Score AgPAQ Non-AgPAQ Technical Content Score AgPAQ Non-AgPAQ

M

SE

Lower Bound

Upper Bound

15.00 8.21

.00 .67

4.77

8.80

21.86 13.43

1.82 1.82

2.37

14.48

Conclusions and Recommendations Participation in an integrated four-course-cluster learning community grounded in agriculture—specifically agronomy and agricultural economics—made a significant, positive difference in written communication skills, oral communication skills, and agronomic/economic technical content knowledge attained by upper level college of agriculture students. This conclusion was consistent with previous work supporting the theory that participation in learning communities can improve communication as well as technical content knowledge (Cowen, 2000; Cyphert, 2002; Lichtenstein, 2005; Seels, et al., 2003; Smith & Bath, 2006; Thompson, 1990). Earlier studies determined that learning community participation makes a significant difference in “academic competence, especially in writing” (Lichtenstein, p. 352). Moreover, Smith and 161

Bath’s results add weight to the importance of learning communities when measuring the whole of communication development. Smith and Bath also measured the effect of learning community participation on discipline knowledge—disciplinary-specific knowledge or technical content knowledge— and found that development of discipline knowledge was significant when measured within learning community environments. Faculty interested in enhancing students’ oral communication skills, written communication skills, and technical content knowledge should consider organizing an integrated course cluster learning community that features a common theme across courses. Course instructors should meet as a team to coordinate syllabi, develop joint assignments, and plan activities focused on the learning community’s common educational goals. Because of the limited scope and focus of this study, caution should be exercised in generalizing results. Further research is needed to more definitively evaluate the effect of upperlevel integrated course cluster learning communities. Focusing on the degree of integration may show that a full four-course integration may not be necessary to make a significant difference on written communication skills, oral communication skills, or technical content knowledge. Future research could include parallel studies that incorporate qualitative methods to complement quantitative results. Researchers might also consider situating learning communities in different major areas of study in agriculture, and incorporating variables such as learner and instructor satisfaction, group dynamics, problem-solving skills, levels of participation, and leadership skills. References Boyer Commission on Educating Undergraduates in the Research University. (1998). Reinventing undergraduate education: A blueprint for America’s research universities. Retrieved May 31, 2004, from http://naples.cc.sunysb.edu/Pres/boyer.nsf. Campbell, D. T., & Stanley, J. C. (1963). Experimental and quasi-experimental designs for research. Chicago: Rand McNally. Cove, P. G., & Love, A. G. (1996). Enhancing student learning: Intellectual, social, and emotional integration. (Report No. ASHE-ERIC HERS 95-4). Washington, DC: ERIC Clearinghouse on Higher Education. (ERIC Document Reproduction Service No. ED400741). Cowen, S. (1999). Assessment of the new general education program, Hayward, CA: California State University. Cremin, L. (1962). The transformation of the school: Progressivism in American education, 1876–1957. New York: Alfred A. Knopf.

162

Cyphert, D. (2002). Integrating communication across the MBA curriculum. Business Communication Quarterly (Focus on Teaching), 65(3), 81-86. Dewey, J. (1933). How we think: A restatement of the relation of reflective thinking to the educative process. Boston: Houghton Mifflin. Dewey, J. (1938) Experience and education. New York: Collier Books. Gabelnick, F., MacGregor, J., Matthews, R. S., & Smith, B. L. (1990). Learning communities: Creating connections among students, faculty, and disciplines. New Directions for Teaching and Learning, 41. San Francisco: Jossey-Bass. Gokhale, A. A. (1995). Collaborative learning enhances critical thinking. Journal of Technology Education, 7(1), 22–30. Hanson, D., and Rawlinson, M. C. (2003). Learning communities as a strategy for general education in a public research university. Stony Brook, NY: Stony Brook University. Kolb, D. A. (1984). Experiential learning. Englewood Cliffs, NJ: Prentice-Hall. Lichtenstein, M. (2005). The importance of classroom environments in the assessment of learning community outcomes. Journal of College Student Development, 46(4), 341-356. Ravitch, D. (1983). The troubled crusade: American education, 1945–1980. New York: Basic Books. Secretary’s Commission on Achieving Necessary Skills (SCANS). (1991). What work requires of schools: A SCANS report for America 2000. Washington, DC: United States Department of Labor. Retrieved July 8, 2004, from http://wdr.doleta.gov/SCANS/whatwork/ Seels, B., Campbell, S., & Talsma, V. (2003). Supporting excellence in technology through communities of learners. Educational Technology Research and Development, 51(1), 91104. Smith, C., & Bath, D. (2006). The role of the learning community in the development of discipline knowledge and generic graduate outcomes. Higher Education, 51, 259-286. Smith, B. L., MacGregor, J., Matthews, R. S., & Gabelnick, F. (2004). Learning communities: Reforming undergraduate education. San Francisco: Jossey-Bass. Sterba-Boatwright, B. (2000). The effects of mandatory freshman learning communities: A statistical report. Assessment Update. San Francisco: Jossey-Bass.

163

Taylor, K., Moore, W. S., MacGregor, J., and Lindblad, J. (2003). Learning community research and assessment: What we know now. National Learning Communities Project Monograph Series. Olympia, WA: The Evergreen State College, Washington Center for Improving the Quality of Undergraduate Education, in cooperation with the American Association for Higher Education. Thompson, K. (1990). Learning at Evergreen: An assessment of cognitive development using the Perry model. Olympia, WA: The Evergreen State College. Tinto, V. (2000). Learning better together: The impact of learning communities on student success. Journal of Institutional Research, 9(1), 48-53. Zhao, C., & Kuh, G.D. (2004). Adding value: Learning communities and student engagement. Research in Higher Education, 45(2), 115-138. Zilversmit, A. (1993). Changing schools. Chicago: University of Chicago Press. Authors Cynthia Barnett is a 4-H Youth Development Advisor with the University of California Cooperative Extension, 777 E. Rialto Avenue, San Bernardino, CA, 92415. Email: [email protected]. Phone: 909-387-2193. Fax: 909-387-3306. Greg Miller is a Professor in the Department of Agricultural Education and Studies at Iowa State University, 201 Curtiss Hall, Ames, IA, 50011. Email: [email protected]. Phone: 515-294-2583. Fax: 515-2940530. Thomas A. Polito is the Director of Student Services in the College Agriculture and Life Sciences at Iowa State University, 23 Curtiss Hall, Ames, IA, 50011. Email [email protected]. Phone: 515-294-2766. Fax: 515-294-2844. Lance Gibson is a Research Analyst with Pioneer Hi-Bred International, Inc., 7200 NW 62nd Avenue, PO Box 184, Johnston, IA, 50131. Email: [email protected]. Phone: 515-334-4491. Fax: 515-270-3346.

164

MAJOR COMPARISON OF COGNITIVE POTENTIAL: ARE AGRICULTURE STUDENTS DIFFERENT? Emily B. Rhoades, Ohio State University John C. Ricketts, University of Georgia Curtis R. Friedel, Louisiana State University Abstract Given the interest, research, and effort extended to help faculty in colleges of agriculture provide educational discourse at higher cognitive levels over the last few years, one would expect that students enrolled in colleges of agriculture would exhibit higher levels of critical thinking and need for cognition. This study thus aimed to discover if the cognitive potential of students enrolled in colleges of agriculture did in fact differ from students enrolled in other colleges. Findings suggest that students enrolled in agriculture had significantly lower GPA, critical thinking disposition, and need for cognition when compared to students not in agriculture. Further research is needed to determine how instructors are integrating critical thinking into the classroom, as well as instructors’ level of cognition. It is recommended that further work be done to increase college of agriculture students’ cognitive abilities to help them be prepared for today’s world. Introduction Glaser’s studies in the 1940s, Facione’s research in the 1990s, and the many others over the years who have explored the ideas and philosophical groundings of cognitive processing and critical thinking have all encouraged educators to find ways to engage students in more meaningful, deeper levels of thought. Research on cognition and critical thinking can be found in literature ranging from feminism, humanities, nursing, and business to science and agricultural education. No matter the discipline, the message from the research is the same: students must be engaged to delve deeper into topics and look critically at knowledge. That message has never been more important than in today’s world of information overload, limited resources, and international competition where students must be prepared to employ deeper cognitive processing when faced with ethical, social, economic, and professional issues. While the body of knowledge on how to increase student’s cognitive abilities is large in breadth, the field of agricultural education, specifically, has focused on furthering cognitive skills in the classrooms of colleges of agriculture for many years. Edgar and colleagues noted in their 10-year look at the Journal of Agricultural Education that critical thinking was the sixth most published research topic (Edgar, Edgar, Briers, & Rutherford, 2008). Prolific authors in the field have all chimed in to further our knowledge on how to increase critical thinking skills and dispositions, as well as other variables involved in the cognitive process (Burris & Garton, 2006; Friedel, Irani, Rudd, Gallo, Ricketts, & Eckhardt, 2008; Hedges, 1991; Moore, Rudd, & Penfield, 2002; Myers & Dyer, 2006; Ricketts & Rudd, 2004A; Ricketts & Rudd, 2004B; Rudd, Baker, & Hoover, 2000; Torres & Cano, 1995). Cognition researchers outside of agricultural education have determined little difference among majors (Broadbear, Jin, & Bierma, 2005). However, it remains to be seen how much this research has affected students’ skills and dispositions in critical thinking and cognition in colleges of agriculture. This study aims to 165

compare students majoring in agriculture with those in non-agriculture disciplines to gauge cognitive impact at five separate universities. With the heavy push by researchers in agricultural education to teach at higher levels, it could be argued that students in these colleges should learn at higher cognition than students in other colleges. If this is not the case, then more work is needed in furthering cognitive processing with students in colleges of agriculture. Theoretical Framework Critical Thinking Critical thinking is defined in different ways from many different scholars in many different fields. Facione (1990), who conducted a national Delphi study to ultimately define and frame a concept of critical thinking characterized it as “purposeful, self-regulatory judgment, which results in interpretation, analysis, evaluation, and inference, as well as explanation of the evidential, conceptual, methodological, criteriological, or contextual considerations upon which that judgment is based” (p. 2). In agricultural education, an often cited description of critical thinking is the one provided by Rudd, et al. (2000). They believed critical thinking was “a reasoned, purposive, and introspective approach to solving problems or addressing questions with incomplete evidence and information and for which an incontrovertible solution is unlikely” (p. 5). Just about every academician and every professional with a connection to education would not only claim critical thinking is important, but they would also argue they are indeed critical thinkers themselves. However, critical thinking is not so easily attained. According to VanGelder (2005) and Kuhn (1991) humans are not built with an inborn capacity for being critical. Critical thinking is actually a multi-dimensional concept consisting of skills (i.e. the ability to analyze or make inferences), dispositions (i.e. a tendency to wonder or a character of understanding), and knowledge (i.e. a mastery of pedagogy in agricultural education) (Mason, 2007).

Figure 1. Expert concepts of critical thinking. 166

Research conducted in agricultural education tends to suggest that the conceptual framework for critical thinking includes skills, dispositions, and knowledge. Conceptually, Facione (1990) agrees that critical thinking includes both skills and dispositions. He believed the requisite critical thinking skills to be interpretation, analysis, evaluation, inference, explanation, and self-regulation. Likewise, he believed that the important critical thinking dispositions were analyticity, self-confidence, inquisitiveness, maturity, open-mindedness, systematicity, and truth seeking. Moore, Rudd, and Penfield (2002) factor analyzed Facione’s disposition suggestions and determined that the respective constructs failed to group together adequately. In response, Irani, Rudd, Gallo, Ricketts, Friedel, and Rhoades (2007) developed a three-component model of critical thinking disposition, which is based on the literature and supported with psychometric analysis: engagement, cognitive maturity, and innovativeness. It would be hard to argue for a simpler model given the historical and theoretical complexity of critical thinking as an area of study. Consider that critical thinking and its origins date back to Socrates and Plato, Aquinas in the Middle Ages, Bacon and his suggestion for “empirical” study, Descartes and his dictate to discipline the mind, and even to a favorite of many in agricultural education – John Dewey. From [Dewey’s] work, we have increased our sense of the pragmatic basis of human thought (its instrumental nature), and especially its grounding in actual human purposes, goals, and objectives. From the work of…Wittgenstein we have increased our awareness not only of the importance of concepts in human thought, but also of the need to analyze concepts and assess their power and limitations. From the work of Piaget, we have increased our awareness of the egocentric and sociocentric tendencies of human thought and of the special need to develop critical thought, which is able to reason within multiple standpoints, and to be raised to the level of "conscious realization." From the massive contribution of all the "hard" sciences, we have learned the power of information and the importance of gathering information with great care and precision, and with sensitivity to its potential inaccuracy, distortion, or misuse. From the contribution of depth psychology, we have learned how easily the human mind is self-deceived, how easily it unconsciously constructs illusions and delusions, how easily it rationalizes and stereotypes, projects and scapegoats. (Paul, Elder, & Bartell, 2008, p. 19). Critical thinking has justifiably become an expectant outcome in education. Benefits of heightened critical thinking skill and disposition include improved listening and respect for different ideas, interest in learning, feelings of accomplishment, and nurtured teamwork, communication, and speaking skills (Yang & Chung, 2007). Critical thinking in students is positively and significantly related to leadership development (Ricketts, 2005), grades in school (Burris & Garton, 2006; Ricketts, 2003), and even success in high stakes testing (Williams, Schmidt, Tilliss, Wilkins, & Glasnapp, 2006). With the seemingly impactful nature of critical thinking, it is reasonable that every educator claims to foster and utilize critical thinking. It would also be helpful if this was the case. In fact, Chang and Yang (2006) conducted a teacher education study and found that teachers need to be proficient users of critical thinking if students are to also adopt the practice.

167

Do all educators assume a paradigm of collaboration between themselves, students, and dependent industry leaders? According to West, Bross, and Snyder (2007), this type of collaboration is necessary for the development of critical thinking. Do all educators insist on active learning or try to incorporate a measure of service learning? Burbach, Matkin, and Fritz (2004) determined that active learning improves critical thinking, and Joseph, Stone, Grantham, Harmancioglu, and Ibrahim (2007) discovered that one of the positive attributes of service learning was improved cognition. Educators seeking to develop critical thinking have much to consider. Educators need to make sure they are both infusing critical thinking into the curriculum and that they are overtly teaching thinking strategies (Case, 2005; Friedel, Irani, Rudd, Gall, & Eckhardt, 2006). Educators also need to encourage students to concentrate on critical thinking development over the long-haul (Bartlett & Cox, 2002). Critical thinking development takes continued focus. Need for Cognition However, some areas needed for critical thinking cannot always be taught. Cognition, for example is something that develops over time based on experiences and environment. Cacioppo and Petty (1982) described cognition as an individual’s inclination to think through events holistically, while one’s need for cognition (NFC) is their inclination to elaborate on events and think about them as they search for a reality. NFC has been related to intelligence (Cacioppo & Petty, 1982), academic performance, course grades (Leone & Dalton, 1988; Sadowski & Gulgoz, 1996), learning style (Haugtvedt, Petty, & Cacioppo, 1992), and to critical thinking dispositions (Friedel, Rhoades, Ricketts, Stedman, Irani, 2008). However, it has been found that gender has no effect nor is it related to abstract or verbal reasoning (Cacioppo, Petty, & Morris, 1983). NFC has been shown to be a tendency that develops through one’s experiences and endeavors requiring cognitive thought. Researchers have noted that those who are high in their need for cognition will think more in-depth about arguments presented to them, and will see weaker arguments as unfavorable (Cacioppo & Petty, 1982; Haugtvedt, Petty, & Cacioppo, 1992). Those who are lower in NFC will scrutinize communication less and will tend to avoid anything that requires effortful, cognitive work. Much research has looked at how NFC can change one’s attitude, and it has been noted that for those low in NFC, their attitude can change because of a simple cue. While those who are higher in NFC will change their attitude based on the merit of the relevant arguments presented to them (Haugtvedt & Cacioppo, 1992). A Call for Higher Level of Thinking Higher order thinking skills, which require students to engage in problem solving and critical thinking processes, have been a research staple in the agricultural education literature over the years. To reiterate, it has been found that students who develop higher levels of cognitive thinking will do better academically. According to, Whittington (1995) in order to foster this in students, it must be fostered in the instructors. The ability to demonstrate higher levels of thinking and problem solving during class can depend heavily on the instructor. In 1993, Whittington and Newcomb explored the cognitive level teachers in a college of agriculture aspired to teach at, and what level they were actually teaching. They noted that while these 168

instructors had positive attitudes toward and aspirations to teach and test at higher levels of cognition, they were not meeting those goals. Many instructors were conducting the course at lower levels of cognition. It was concluded that some instructors may not fully understand the long-term affects of using higher level cognition in the classroom, and the changes that must be made to their curriculum to engage students at that level. Whittington echoed the findings in 1995, noting that while instructors wanted to engage students at all levels, they tended to mostly have discourse at a lower level. In fact, instructors in this study conducted discourse at a lower level 98% of the time. Several studies over the years have noted these concerns and indicated that instructors may feel that they do not have the time or experience needed to rethink lesson plans and assessments to engage students at higher levels of thinking. Researchers have continually encouraged faculty in colleges of agriculture to present workshops and seminars to assist other faculty in learning the techniques needed to reach these higher levels of cognition (Whittington, 1995; Whittington, Stup, Bish, & Allen, 1997; McCormick, Whittington, 2000; Miller & Pilcher, 2001; Ewing, Carnes, & Whittington, 2006). Numerous academicians have heeded this call and presented workshops, seminars, and teaching and learning groups to help colleagues in their colleges rethink how they prepare and teach courses to hit at these higher levels of thinking. However, it has yet to be researched how effective these calls have been in actually increasing cognitive thinking in students in colleges of agriculture. If college instructors aspire to teach at higher levels of thinking to engage their students, and if they are receiving help in preparing their classes as such, it could be assumed students would be benefiting. It is important to understand how students in colleges of agriculture are faring in terms of their cognitive potential compared to students outside of such colleges. Are they similar, are they better? In order to continue improving education in colleges of agriculture, we must know the answer. Research in higher-level thinking has provided evidence that these skills are domain specific (Huitt, 1998). That is, one can exhibit high levels of critical thinking in one domain of knowledge and not be able to transfer those skills to another. This presents a difficulty in consistently measuring cognitive skills of students in colleges of agriculture, because the diversity of agriculture incorporates many different domains. However, one can measure students’ disposition towards thinking and their desire for thinking outside the context of a knowledge domain (Facione, Giancarlo, Facione, & Gainen, 1995). Further, dispositions and desires for thinking are fostered through the practice of thinking (Tishman & Andrade, 1996). One may assume from this that high levels of critical thinking disposition and need for cognition are related to the practice of using higher level thinking skills in classrooms located in colleges of agriculture. Given the interest, research, and effort extended to help faculty in colleges of agriculture provide educational discourse at higher cognitive levels, one would expect that students enrolled in colleges of agriculture would exhibit higher levels of critical thinking and need for cognition. The disposition and desire to use higher level thinking skills are necessary for the employment of those skills (Norris, 1994), which suggests that the measurement of these cognitive attitudes provide indication of the potential in learning cognitive skills. Does the cognitive potential of students enrolled in colleges of agriculture differ from students enrolled in other colleges?

169

Purpose Based on the plethora of research in the field of education and agricultural education on the need to further students’ cognitive development and skills, this study aims to discover how far agriculture educators have come in improving our students’ disposition to using critical thinking compared to students not majoring in colleges of agriculture. The study also seeks to determine if differences exist between students’ need for cognition and grade point average among students based on their enrollment in a college of agriculture. The outlined theoretical framework served as the guiding structure in which the researchers have developed the following hypotheses to be tested: H01 There is no difference in critical thinking disposition between students who are agricultural majors and those who are non-agricultural majors. H02 There is no difference in need for cognition between students who are agricultural majors and those who are non-agricultural majors. H03 There is no difference in grade point averages between students who are agricultural majors and those who are non-agricultural majors. Methods This quantitative study sampled participants from four service courses taught in colleges of agriculture at four land-grant universities. The researchers selected courses, which traditionally have had students from a variety of majors, academic ability, and class rank. Direct administration of instrumentation measuring critical thinking, need for cognition, and selected demographics resulted in 317 respondents. Due to the non-random sample, results cannot be generalized past these courses. However, this study incorporated what was conceptualized as a slice in time (Oliver & Hinkle, 1981) sampling of students. This type of sampling (convenience) has been justified by Gall, Borg, and Gall (1996). Instrumentation Two instruments testing cognitive potential were used in the study along with questions on gender, age, major, and GPA. The UF-EMI, a 26-item instrument, gauged student critical thinking disposition through three constructs: engagement, cognitive maturity, and innovativeness (Irani, et al., 2007). The combined score of the rating scale (i.e. Likert) instrument can range from 26 points (a low critical thinking disposition) to 130 points (a high critical thinking disposition). Instrument developers report an overall reliability of .92 (Irani, et al., 2007). Cronbach’s alpha was run post hoc in this study and found an overall reliability of .92. The UF-EMI also asked questions regarding students’ demographic information and GPA. A student’s “tendency to engage and enjoy effortful cognition” was measured with the Need For Cognition Scale (Cacioppo, Petty, & Kao, 1984, p. 306). Cacioppo and colleagues’ 18item instrument utilizes a five-point summated rating scale. An overall summation of items is calculated for the need for cognition score, which has a possible range of 18 points (indicating low NFC) to 90 points (indicating high NFC). Researchers who developed the NFC reported a 170

Cronbach’s alpha reliability coefficient of .90 (Cacioppo, Petty, & Kao, 1984). In this study, post-hoc reliability was calculated and determined as .84. Data Analysis Data were analyzed with the Statistical Package for the Social Sciences (SPSS). Means and frequencies were calculated on demographic variables including age, gender, GPA, total NFC score, and total EMI score. Researchers used independent sample t-tests to test the hypotheses identified by this study. Results Selected demographics of the 317-person sample were identified using questions from the UF-EMI. Participants ranged in age from 18 to 35 years with a mode of 21 years. The majority of the sample was female (56.2%, n = 178). The majority of students in the four courses were seniors (n=156, 49%), followed by juniors (n = 116, 37%), sophomores (n = 35, 11%), and freshman (n = 10, 3.2%). Only 13% (n = 42) indicated being part of an honors program, and the mean GPA was 3.24. Participants of this study reported being in a variety of 57 majors, which ranged from food science to English. The top number of majors included animal science (n = 33, 10%), construction systems management (n = 29, 9%), and family youth and consumer sciences (n = 23, 7%). Students’ academic majors were coded to distinguish whether or not they were affiliated with a college of agriculture at their respective university. Findings indicated that 178 students (56.2%) had majors found in a college of agriculture, while 139 students (43.8%) were working toward a degree not related to agriculture. The demographic information gathered on these participants indicated that most of these students were traditional undergraduate students and predominately juniors or seniors. The first hypothesis proposed in this study was that there was no difference in critical thinking disposition between students majoring in agriculture and students not majoring in agriculture. Critical thinking disposition scores, as measured by the UF-EMI, for this sample of undergraduate students ranged from 48 to 130 with a mean of 100.19 points. A two-tailed independent sample t-test was conducted to determine if critical thinking disposition scores differed between students in agricultural academic majors and students not in agricultural academic majors. Levene’s Test for Equal Variance was performed to test for equal variance between the two groups. The results indicated to reject the null hypothesis (F = 5.43, p = .02) and concluded that these two groups of students were not equal in variance. Therefore, the researchers interpreted the t-statistic calculated by SPSS when equal variances are not assumed. A significant difference was found (t = 3.85, p = .00) among total critical thinking disposition scores between students enrolled in an agricultural academic majors (M = 97.81) and nonagricultural academic majors (M = 103.25). Considering the difference is approaching a medium effect size (Cohen’s d = .43) (Cohen, 1992), the null hypothesis was rejected and it is concluded the two groups are significantly different in their critical thinking skill disposition. That is, these students enrolled in a college of agriculture have significantly lower levels of critical thinking disposition than those students not enrolled in a college of agriculture. (See Table 1)

171

Table 1 Differences in Critical Thinking Disposition by College Affiliation Major df M SD T Agriculture majors

97.81

13.73

103.25

11.42

100.19

13.03

3.85

313.71

p

Cohen’s d

.00

.40

(n = 178) Non-agriculture majors (n = 139) All Students (n = 317)

Note. Critical thinking disposition was measured by the UF-EMI with 26 items. The possible range for total critical thinking disposition was 26, indicating a low level of critical thinking disposition to 130,indicating high level of critical thinking disposition. The second hypothesis identified in this study was that there is no difference in need for cognition between students who are agricultural majors and those who are non-agricultural majors. The NFC scale was used to determine students’ need for cognition. For this group of students, scores ranged from 24 to 83 points (M = 60.44). To test the second hypothesis, a twotailed t-test was performed. Levene’s Test for Equal Variance was calculated to determine if the assumption of equal variance between these two groups was met. There was no significant difference (F = 1.51, p = .22), indicating a failure to reject the null hypothesis and equal variances can be assumed. Results of the t-test indicated a significant difference (t = 2.96, p = .00) between these students who were categorized by either being enrolled in a college of agriculture (M = 58.99) or not enrolled in a college of agriculture (M = 62.29). These findings provided evidence to reject the second null hypothesis and conclude that among these students, those enrolled in a college of agriculture have significantly lower NFC scores than those not enrolled in a college of agriculture. It should be noted that the difference had a small effect size (Cohen’s d = .34) (See Table 2). Table 2 Differences in Need for Cognition by College Affiliation Major M SD T

df

P

Agriculture majors

315

.00

58.99

10.36

62.29

9.08

60.44

9.95

2.96

(n = 178) Non-agriculture majors (n = 139) All Students

172

Cohens’s d .34

(n = 317) Note. Need for cognition was measured by the NFC with 18 items. The possible range for total need for cognition was 18–indicating low need for cognition to 90–indicating high need for cognition. The third hypothesis of this study stated that there is no difference in grade point averages between students who are agricultural majors and those who are non-agricultural majors. A selfreported GPA was collected from participating students during test administration of the UFEMI. Among these students, GPAs ranged from 1.9 to 4.0 with a mean of 3.24 on a 4.0 scale. A two-tailed t-test was utilized to test this hypothesis. Again, Levene’s Test for Equality of Variances was used to determine if equal variances among the two groups’ grade point averages could be assumed. The test suggested that there was no significant difference (F = .21, p = .65) and it was concluded to fail to reject the null hypotheses and assume equal variance for these scores. The t-test performed to test the third hypothesis in this study indicated a significant difference (t = 3.37, p = .00) in GPAs between students enrolled in a college of agriculture (M = 3.16) and students not enrolled in a college of agriculture (M = 3.33) at these four land-grant universities. From these findings, it was concluded that participating students enrolled in colleges of agriculture had significantly lower self-reported GPAs than students not enrolled in colleges of agriculture. (See Table 3) Table 3 Differences in Self-reported GPA by College Affiliation Major M SD T

df

p

Agriculture majors

315

.00

3.16

0.44

3.33

0.44

3.24

0.45

3.34

Cohens’s d .39

(n = 178) Non-agriculture majors (n = 139) All Students (n = 317) Note. Grade point average was determined as a self-reported average on a 4.0 scale. Conclusions/Recommendations While this study cannot be generalized past these four universities, it is important to note that the students represented a variety of ages, class ranks, and majors in and out of agriculture. While students in agriculture were lower in their cognitive abilities, it is also important to note that the overall averages for critical thinking disposition and need for cognition were not low on the scales and were actually moderate to high. However, agriculture majors in this study did score significantly lower on critical thinking dispositions than their non-agriculture major counterparts. Studies have been conducted to 173

determine critical thinking differences between majors within related disciplines (Ricketts, Pringle, & Douglas, 2007; Broadbear, et al., 2005), but this is the first known study of its kind to determine critical thinking differences between students majoring in a college of agriculture and those majoring in other fields. Given the strong science underpinnings of many agricultural disciplines and specific attention given to developing critical thinking at the respective universities, this finding was unexpected. Research should be conducted that determines the extent of overt and infused focus on the development of critical thinking in the respective colleges of agriculture. Are faculty incorporating active learning strategies and service learning activities, which are known to develop critical thinking (Burbach, et al., 2004; Joseph, et al., 2007)? Research should also determine the critical thinking skills and dispositions of faculty members in colleges of agriculture. Chang and Yang (2006) and Whittington (1995), and researchers in teacher education, would agree – faculty need to be proficient users of critical thinking if students are going to adopt it. Agriculture majors in this study scored significantly lower on Need for Cognition as well. This finding was not surprising when taken with the other findings of this study. Research has shown that need for cognition is related to academic achievement and critical thinking disposition (Friedel, et al., 2008, Leone & Dalton, 1988; Sadowski & Gulgoz, 1996). An individual’s need for cognition is developed through experiences, which require them to engage in deeper cognitive thought (Cacioppo & Petty, 1982). It could thus be assumed that if these students had lower NFC then they may have been exposed to less situations that require deeper cognition than the non-agriculture students. As with critical thinking needs in the classroom, it is important that we understand how teachers are requiring this deeper thought in their classrooms. Whittington (1995) noted that many instructors feel they are giving their students these experiences, when in reality they are not. It is important that we continue to work with these instructors to ensure they are infusing activities that require critical thinking and deep cognition of the subject. Agriculture majors also had significantly lowers GPAs than non-agriculture majors. It is improbable that instructors in colleges of agriculture grade harder, or inflate grades less. Rather it is more feasible that these students are struggling more academically. Granted, a significant amount of science is included in a degree in agriculture, but the majority of the participants were juniors and seniors so the assumption can be made that the majority of core competencies had been met. Other researchers have noted the relationship between critical thinking and grade point average (Ricketts, 2003; Torres, 1993). Therefore, the finding that agriculture students had lower critical thinking dispositions and lower grade point averages makes sense. However, this ought to be a concern if graduates of colleges of agriculture are to be competitive with non-agriculture majors. Faculty and academic administrators should consider an organized effort to improve critical thinking and need for cognition. This effort should improve the academic success of college of agriculture students. It is important to note that GPAs used were self-reported rather than actual GPAs obtained from the students’ respective universities. Student self-reported items may be inflated due to students overestimating their performance to be perceived as better, also known as the halo 174

effect. However, research has indicated that the halo effect is constant across students and schools (Pike, 1999). Therefore, if values reported in this study were less than authentic, there was no advantage given to either students enrolled in colleges of agriculture or students not enrolled in colleges of agriculture. Further research is needed to further explore the cognitive differences between students in colleges of agriculture and those not in colleges of agriculture. Further studies should be conducted at other universities and in other courses to see if similar findings result. Research is also needed to explore if differences exists within majors in colleges of agriculture to see if there are differences between social science students and those in the natural sciences. As indicated earlier, studies must be conducted with instructors in colleges of agriculture to determine their cognitive ability, and their level of infusing critical thinking into their courses. While much research has been done in the field of agriculture education to encourage such integration into courses, it is obvious that more work is still needed, theoretically and practically. References Bartlett, D. J. & Cox, P. D. (2002). Measuring change in students’ critical thinking ability: Implications for health care education. Journal of Allied Health, 31(2), pp. 64-69. Broadbear, J.T., Jin, G., & Bierma, T.J. (2005). Critical thinking dispositions among undergraduate students during their introductory health education course. Health Educator, 37(1), 8-15. Burbach, M. E., Matkin, G. S., & Fritz, S. M. (2004). Teaching critical thinking in an introductory leadership course utilizing active learning strategies: A confirmatory study. College Student Journal, 38(3), p. 482. Burris, S., & Garton, B.L. (2006). An investigation of the critical thinking ability of secondary agriculture studnets. Journal of Southern Agricultural Education Research, 56(1), 18-29. Cacioppo, J. T., & Petty, R. E. (1982). The need for cognition. Journal of Personality and Social Psychology, 42, 116-131. Cacioppo, J. T., Petty, R. E., & Kao, C. F. (1984). The efficient assessment of "need for cognition." Journal of Personality Assessment, 48, 306-307. Cacioppo, J. T., Petty, R. E., & Morris, K. (1983). Effects of need for cognition on message evaluation, argument recall, and persuasion. Journal of Personality and Social Psychology, 45, 805-818. Case, R. (2005). Bringing critical thinking to the main stage. Education Canada, 45(2), pp. 4549.

175

Chang, Y. & Yang, Y. (2006). Critical thinking instruction model in teacher education. In T. Reeves & S. Yamashita (Eds). Proceedings of the World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education 2006, Chesapeake, VA, pp. 1131-1135. Cohen, J. (1977). Statistical power analysis for the behavioral sciences. (Rev. ed.).New York: Academic Press. Edgar, L.D., Edgar, D.W., Briers, G.E., & Rutherford, T. (2008, May). Research themes, authors, and methodologies in the Journal of Agricultural Education: A ten year look. Proceedings of the 35th National Agricultural Education Research Conference, Reno, Nevada, pp. 89-104. Ewing, J.C., Carnes, A., & Whittington, M.S. (2006, May). Type, number, and cognitive level of academic challenges provided by professors. Paper presented at the National Agricultural Education Research Conference, Charlotte, N.C. Facione, P.A. (1990). Critical thinking: A statement of expert consensus for purposes of educational assessment and instruction. Millbrea, CA: The California Academic Press. Facione, P.A., Giancarlo, C.A., Facione, N.C., & Gainen, J. (1995). The disposition toward critical thinking. Journal of General Education, 44(1), 1-25. Friedel, C., Irani, T., Rudd, R., Gallo, M., Ricketts, J. C., & Eckhardt, E. (2008). Overtly teaching critical thinking and inquiry-based learning: A comparison of two undergraduate biotechnology classes. Journal of Agricultural Education, 49(1), 72-84. Friedel, C., Rhoades, E., Ricketts, J., Stedman, N., Irani, T. (2008, May). Relationships between critical thinking disposition and need for cognition among undergraduate students enrolled in leadership courses. Proceedings of the 35th National Agricultural Education Research Conference, Reno, Nevada, pp. 840-854. Gall, M. D., Borg, W. R., & Gall, J. P. (1996). Educational research: An introduction (6th ed.). New York: Longman. Haugtvedt, C.P., Petty, R.E., & Cacioppo, J. T. (1992). Need for cognition and advertising: Understanding the role of personality variables in consumer behavior. Journal of Consumer Psychology, 1, 239-260 Haugtvedt, C. P., & Petty, R. E. (1992). Personality and persuasion: Need for cognition moderates the persistence and resistance of attitude changes. Journal of Personality and Social Psychology, 63, 308-319. Hedges, L.E. (1991). Helping students develop thinking skills through the problem-solving approach to teaching. The Ohio State University, Dr. Lowell Hedges. 176

Huitt, W. (1998). Critical thinking: An overview. Educational Psychology Interactive. Valdosta, GA: Valdosta State University. Retrieved October 25, 2006, from http://chiron.valdosta.edu/whuitt/col/cogsys/critthnk.html Irani, T., Rudd, R., Gallo, M., Ricketts, J., Friedel, C., & Rhoades, E. (2007). Critical thinking instrumentation manual. Retrieved December 1, 2007, from http://step.ufl.edu/resources/critical_thinking/ctmanual.pdf. Joseph, M., Stone, G. W., Grantham, K., Harmanciouglu, N., & Ibrahim, E.(2007). An exploratory study on the value of service learning projects and their impact on community service involvement and critical thinking. Quality Assurance in Education, 15(3), 318-333. Kuhn, D. (1991). The skills of argument. Cambridge: Cambridge University Press. Leone C., & Dalton, C.H. (1988). Some effects of the need for cognition on course grades. Perceptual and Motor Skills, 67(1), 175-178. McCormick, D.F., & Whittington, M.S. (2000). Assessing academic challenges for their contribution to cognitive development. Journal of Agricultural Education, 41(3), 114-121. Miller, G., & Pilcher, C.L. (2001). Levels of cognition reached in agricultural distance education courses in comparison to on-campus courses and to faculty perceptions concerning an appropriate level. Journal of Agricultural Education, 42(1), 21-28. Moore, L., Rudd, R., & Penfield, R.(2002). Scale reliability and validity of the California Critical Thinking Disposition Inventory. Unpublished manuscript, University of Florida, Gainesville. Myers, B., & Dyer, J. (2006). The influence of student learning style on critical thinking skill. Journal of Agricultural Education, 47(1), 43-52. Norris, S. P. (1994). The meaning of critical thinking test performance: The effects of abilities and dispositions on scores. In D. Fasko (Ed.), Critical thinking: Current research, theory, and practice. Dordrecht, The Netherlands: Kluwer. Oliver, J. D. & Hinkle, D. E. (1981). Selecting statistical procedures for agricultural education research. Paper presented at the eighth annual National Agricultural Education Research Meeting, Atlanta, GA. Paul, R., Elder, L., & Bartell, T. (2008). A brief history of the idea of critical thinking. Retrieved January 23, 2008 from http://www.criticalthinking.org/ Pike, G. R. (1999). The constant error of the halo in educational outcomes research. Research in Higher Education, 40, 61-86. Ricketts, J. C. (2005). The relationship between leadership development and critical thinking skills. Journal of Leadership Education, 4(2), 27-41. 177

Ricketts, J. C., Pringle, T. D., & Douglas, J. (2007). Critical Thinking Disposition Differences of Animal Science Students and NonMajors. NACTA Journal, 51(2). Ricketts, J.C., & Rudd, R. (2004A). Critical thinking skills of FFA leaders. Journal of Southern Agricultural Education Research, 54(1), 7-20. Ricketts, J.C., & Rudd, R. (2004B). The relationship between critical thinking disposition and critical thinking skill of selected youth leaders in the National FFA Organization. Journal of Southern Agricultural Education Research, 54(1), 21-33. Rudd, R., Baker, M., & Hoover, T. (2000). Undergraduate agricultural student learning styles and critical thinking abilities: Is there a relationship? Journal of Agricultural Education 41(3), 2-12. Sadowski, C. J. & Gulgoz, S. (1996). Elaborative processing mediates the relationship between need for cognition and academic performance. Journal of Psychology, 130 (3), 303-307. Tishman, S., & Andrade, A. (1996). Thinking dispositions: A review of current theories, practices, and issues. Cambridge, MA: Project Zero, Harvard University. Torres, R. M. (1993). The cognitive ability and learning style of students enrolled in the college of agriculture at The Ohio State University. Unpublished doctoral dissertation, The Ohio State University, Columbus, OH. Torres, R. M., & Cano, J. (1995). Critical thinking as influenced by learning style. Journal of Agricultural Education, 36(4), 54-63. Van Gelder, T. (2005). Teaching critical thinking: Some lessons from cognitive science. College Teaching, 53. Retrieved June 23, 2008 from http://www.questia.com West, M. M., Bross, G., & Snyder, M. (2007). Teaching complex trauma care in a curriculum challenges critical thinking and clinical judgment-How nurses can help. Journal of Trauma Nursing, 14(3), pp. 131-135. Whittington, M.S. (1995). Higher order thinking opportunities provided by professors in college of agriculture classrooms. Journal of Agricultural Education, 36(4), 32-38. Whittington, M.S., & Newcomb, L.H. (1993). Aspired cognitive level of instruction, assessed cognitive level of instruction and attitude toward teaching at higher cognitive levels. Journal of Agricultural Education, 34(2), 55-62. Whittington, M.S., Stup, R.E., Bish, L., & Allen, E. (1997). Assessment of cognitive discourse: A study of thinking opportunities provided by professors. Journal of Agricultural Education, 38(1), 46-53.

178

Williams, K. B., Schmidt, C., Tilliss, T. S., Wilkins, K., & Glassnap, D. R. (2006). Predictive validity of critical thinking skills and disposition for the national board dental hygiene examination: A preliminary investigation. Journal of Dental Education, 70(5), 536-544. Yang, S. C. & Chung, T. Y. (2007). Experimental study of teaching critical thinking in civic education in Taiwanese junior high school. British Journal of Educational Psychology, September. Authors Emily B. Rhoades is an assistant professor in the Department of Human & Community Resource Development at The Ohio State University, 208 Agricultural Administration Bldg., 2120 Fyffe Rd., Columbus, OH 42310 Email [email protected]; Phone (614) 2926321; Fax (614) 292-7007. John C. Ricketts is an associate professor in the Department of Agricultural Leadership, Education and Communication at the University of Georgia.110 Four Towers, Athens, GA 30602-4355, Email [email protected]; Phone (706) 542-8646; Fax (706) 5420262. Curtis R. Friedel is an assistant professor in the School of Human Resource Education & Workforce Development at Louisiana State University.142 Old Forestry Bldg., Baton Rouge, LA 70803-5477, Email [email protected]; Phone (225) 578-2108; Fax (225) 578-5755.

179

CHANGES IN TEACHER SELF-EFFICACY AND PERCEPTIONS OF PREPARATION OF AGRICULTURAL EDUCATION TEACHER CANDIDATES Kattlyn J. Wolf, University of Idaho Daniel D. Foster, The Ohio State University Robert J. Birkenholz, The Ohio State University Abstract The purpose of this study was to describe the changes in teacher self-efficacy throughout agricultural education teacher candidates’ teacher preparation program. Additionally, the researchers sought to describe candidates’ perceptions of their preparation. The population was the teacher candidates who student taught during Fall, 2007. Teacher self-efficacy was assessed at three different points during the teacher preparation program using the Teachers’ Sense of Efficacy Scale (Tschannen-Moran & Woolfolk Hoy, 2001); in the spring following the teacher preparation coursework, during the second week of the student teaching internship, and at the conclusion of the student teaching internship. Teachers’ perceptions of their preparation were assessed during the second week and at the conclusion of the student teaching internship. Candidates reported the lowest overall level of teacher self-efficacy during the spring, and the highest level at the end of the internship experience. Candidates reported the least amount of change in the student engagement domain, and the most change in the classroom management domain. The perceptions of candidates with regard to their preparation did not change during the student teaching internship. The researchers concluded that candidates required assistance in the student engagement domain during their coursework and during the student teaching internship. Introduction/Theoretical Foundation Agricultural education at the secondary school level faces a critical teacher shortage. Kantrovich (2007) estimated a teacher deficit of 38.5% in 2007. Of the 785 qualified graduates in 2005-2006, teacher educators estimated that only 69.8% of the graduates planned to enter the profession of agricultural education. The agriculture teacher shortage is not a new trend; “A defacto ‘teacher shortage’ has been a constant problem for agricultural education for at least the 40 years covered by this study” (Kantrovich, 2007, p. 3.). The shortage of qualified teachers has been further complicated by the National Council for Agricultural Education’s 10X15 initiative. This initiative envisions 10,000 quality agricultural education programs in the U.S. by the year 2015. One goal, specific to recruiting highly-qualified educators is to: “Meet the demand for well-trained, highly qualified agricultural educators for all roles within the profession and encourage their involvement in appropriate professional organizations” (Team Ag Ed, 2007, p. 18). Therefore, a challenge facing the agricultural education profession involves simultaneously remediating the current shortage of qualified professionals and preparing additional qualified agricultural educators to meet the goals of the 10X15 initiative. Overcoming the teacher shortage will involve the preparation of future teachers with the belief that they have the potential for success as an agricultural educator. Investigating teacher personal characteristics associated with teacher success and retention in the profession is one essential element to reduce the teacher shortage by improving the rate of retention. In the field of agricultural education, teacher self-efficacy has been found to be positively associated with 180

teacher retention (Whittington, McConnell & Knobloch, 2006; Knobloch & Whittington, 2003); little additional research has been conducted in the field to validate the relationship between teacher self-efficacy and retention. However, “A strong sense of efficacy can support higher motivation, greater effort, persistence, and resilience. Consequently, helping teachers develop strong efficacy beliefs early in their career will pay lasting dividends” (Woolfolk Hoy & Hoy, 2009). Therefore, this investigation of teacher self-efficacy among teacher education candidates is important and significant in agricultural education as the profession attempts to recruit and retain new teachers. Bandura’s social cognitive theory (Bandura, 1986) and the associated theory of selfefficacy (Bandura, 1997) provided the theoretical foundation for this study. Social cognitive theory is rooted in the view that individuals are agents proactively engaged in their own development and can make things happen by their actions. Key to social cognitive theory is the fact that, aside from personal and environmental factors, individuals possess self-beliefs that enable them to exercise a measure of control over their thoughts, feelings, and actions. The idea that an individual has the potential to influence change, regardless of his/her skills, is central to social cognitive theory (Pajares, 2002). Bandura (1994) further suggested that individual selfefficacy is derived from four main sources: mastery experiences, physiological and emotional arousal, vicarious experiences, and social persuasion. Self-efficacy, in the context of teachers and teaching is often referred to as teacher selfefficacy. Tschannen-Moran and Woolfolk Hoy (2001) suggested that teacher self-efficacy was a simple idea with significant implications. The authors described teacher self-efficacy as “. . . a judgment about his or her capabilities to bring about desired outcomes of student engagement and learning, even among those students who may be difficult or unmotivated” (p. 1). Teacher self-efficacy is related to teacher behavior, level of effort, enthusiasm, planning, resoluteness, creativeness, willingness to work with more difficult students, and commitment to teaching (Tschannen-Moran, Woolfolk Hoy, & Hoy, 1998). Teachers with a high sense of self-efficacy believe they can overcome problems through time and effort, while teachers with a low sense of self-efficacy are typically beset with discipline issues and resort to punitive methods of classroom management. Teachers with a low sense of teacher self-efficacy believe that little can be done to reach unmotivated students, and that their influence as a teacher is limited by environmental factors that are beyond their control. Conversely, a teacher with a high sense of teacher self-efficacy is more inclined to create a dynamic, student-centered learning environment in which students take ownership of their learning; whereas teachers with a low sense of selfefficacy would likely devote more time to non-academic, managerial tasks (Bandura, 1997). Teacher preparation is an important factor in teacher self-efficacy. If teachers do not feel that they are adequately prepared to perform a task, they likely not succeed at that task. Knobloch and Whittington (2002) found that teacher preparation quality was associated with student teacher sense of teacher self-efficacy. Ross, Cousins and Gadalla (1996) found that “feelings of being well-prepared” was associated with a sense of teacher self-efficacy. Additionally, Rubecks and Enochs (1991) found teacher self-efficacy was predicted by university coursework related to future teaching requirement. Darling-Hammond, Chung, and Frelow (2002) examined the relationship between perceptions of preparation and teacher selfefficacy and found that ratings of their overall teacher preparedness were significantly related to 181

their sense of efficacy about whether they are able to make a difference in student learning. Teachers in this study who “ . . . felt underprepared were significantly more likely to feel uncertain about how to teach some of their students and more likely to believe that student’s peers and home environments influence learning more than teachers do” (p. 294). Knobloch (2006) found a relationship between student teacher perceptions of their teacher preparation program and their sense of teacher self-efficacy; student teachers who held more positive perceptions of their teacher preparation program were more efficacious at the conclusion of their student teaching experience. Whittington, McConnell, and Knobloch (2006) found that student perceptions of their student teaching experience was positively related (r = .39) to their sense of teacher self-efficacy. Knobloch (2001) reported that early field experiences and teaching peers influenced teacher candidates’ sense of teacher self-efficacy. He suggested that students become more efficacious about their teaching because they had observed and experienced teaching in real settings and had taught their peers. Knobloch and Whittington (2003) studied the self-efficacy of student teachers, first, second, and third-year teachers during the first ten weeks of school. Student teachers were the only group that experienced an increase in self-efficacy during the first ten week period while first-year teachers (as a group) experienced the greatest decline. Rocca and Washburn (2006) investigated differences in self-efficacy between traditionally and alternatively certified teachers. The two groups did not differ in their perceived self-efficacy, however, alternatively certified teachers were about 10 years older than traditionally certified teachers. The researchers questioned why the two groups were similar in their level of selfefficacy, since the alternatively certified teachers did not have formal training in education. However, they did not question the age difference of the two groups, nor did they attribute the results to the age difference of the alternatively certified teachers. Knobloch (2006) found that student teachers at two different institutions reported similarly high levels of teaching selfefficacy; however, the student teachers differed in their perception of environmental factors that contributed to teacher self-efficacy. The environmental factors were: supportive principal behaviors, cooperating teacher competence, and number of class preparations. Knobloch speculated that student teachers may have had an inflated sense of teacher self-efficacy, which remained inflated throughout the student teaching experience as a result of support from their cooperating teachers. Roberts, Harlin, & Ricketts (2006) assessed teacher self-efficacy among student teachers at four different points during a 15-week student teaching experience. The researchers examined the three domains (student engagement, instructional strategies, and classroom management) identified by Tschannen-Moran and Woolfolk Hoy (2001). In the student engagement domain, the students’ teacher self-efficacy scores dropped during the middle of the experience, and were highest at the end of the experience. The instructional strategies domain exhibited a similar pattern. The changes were less pronounced in the classroom management domain but followed the same pattern as the other two domains. The researchers observed that “. . . limited knowledge exists about teaching efficacy of preservice agricultural science teachers, largely due to the paucity of research in this area. Existing research has largely been conducted by just a few researchers, in only a few states” (Roberts, et al., 2006, p. 84). The results of this study were corroborated by a later study that measured teacher self-efficacy of agricultural education teacher candidates at four institutions (Harlin, Roberts, Briers, Mowen, & Edgar, 2007). The teacher 182

candidates assessed exhibited a similar pattern of change in their teacher self-efficacy, with a scores decreasing in the middle of the experience, and increasing toward the end. Roberts, Harlin, & Ricketts (2006) suggested that future research examine the changes in overall teacher self-efficacy in different teacher candidate populations. Additionally, the researchers questioned if different teacher candidate populations were the most efficacious in instructional strategies and the least efficacious in the student engagement domain. Purpose/Objectives The purpose of this study was to assess agricultural education teacher candidates’ perceptions of teacher self-efficacy at different points during their teacher preparation experience and candidates’ perceptions of their level of preparation. The following research objectives guided the study. 1. Describe agricultural education teacher candidates’ teacher self-efficacy at three different points during their teacher preparation experience. 2. Describe agricultural education teacher candidates’ perceptions of their preparation at the beginning of the student teaching internship and at the conclusion of the student teaching internship. 3. Determine the discrepancy between agricultural education teacher candidates’ perceived sense of teacher self-efficacy and level of preparation at the beginning and at the conclusion of the student teaching internship. Methods The population for this descriptive study consisted of the entire cohort of Agricultural Education teacher candidates at a midwestern land-grant university who completed their internship during the fall 2007 academic term. The population frame was identified by the faculty coordinator of the student teaching internship. Twenty-four individuals met the criteria of having completed their ten-week student teaching internship in Agricultural Education during in the fall of 2007. The researchers utilized the Teachers’ Sense of Efficacy Scale (Tschannen-Moran, Woolfolk Hoy, & Hoy, 1998; Tschannen-Moran & Woolfolk Hoy, 2001) to assess the perceived teacher self-efficacy of the agricultural education teacher candidates. This instrument has been extensively tested in studies involving various groups of teachers and pre-service teacher candidates, and subjected to factor analysis procedures to assess construct validity. This study utilized the long summated rating scale (24 items) consisting of three distinct domains: efficacy for instructional strategies (8 items), efficacy for classroom management (8 items), and efficacy for student engagement (8 items). The published reliabilities for each domain were 0.91, 0.90 and 0.87, respectively. Data were collected at three different points during the teacher preparation program. The first assessment was at the conclusion of the spring “Student Teaching Block,” in which the agricultural education teacher candidates enrolled as a cohort group in a series of teacher preparation courses. A second assessment was completed the following fall during the second week of the 10-week student teaching internship. The third and final assessment was 183

performed at the conclusion of the 10-week student teaching internship during an on-campus workshop. The original instrument was adapted assess teacher candidate perceptions of their preparation in the items on the Teachers’ Sense of Efficacy Scale, similar to the Borich (1980) needs assessment model. The teacher self-efficacy scale asked participants to rate each item following the stem: “How much can you do to . . . < Item? >” on a scale from 1 = None, to 9 = A Great Deal. Preparation items asked respondents to rate each item following the stem: “How well prepared were you to . . . < Item? >” on a scale from 1 = Not Prepared to 9 = Very Well Prepared. Discrepancy scores were calculated for each of the three domains (efficacy for instructional strategies, efficacy for classroom management, and efficacy for student engagement) by subtracting the mean preparation score from the mean teacher self-efficacy score in each domain. The spring assessment utilized only the Teacher Efficacy Scale (Tschannen-Moran, Woolfolk Hoy, & Hoy, 1998; Tschannen-Moran & Woolfolk Hoy, 2001). The two assessments during and after the student teaching internship utilized modified instruments that incorporated items relative to the candidate’s perception of their preparation. Data were analyzed using the Statistical Package for the Social Sciences (SPSS). Findings/Results The population consisted of 24 agricultural education teacher candidates. Two-thirds of the respondents were female and one-third were male. The candidates ranged from 21 to 26 years of age. The first research objective was to describe the agricultural education teacher candidates’ sense of teacher self-efficacy. The first assessment was in Spring, 2007, at the conclusion of the student teaching block, the second assessment was during the second week of the student teaching internship, and the final assessment was after the completion of the student teaching internship. Candidates reported the lowest levels (µ = 6.23) of overall teacher self-efficacy (Table 1) at the conclusion of the student teaching block experience, and the highest levels of overall teacher self-efficacy (µ = 7.30) at the conclusion of the student teaching internship. Table 1 Teacher candidate (N=24) perceptions of overall teacher self-efficacy Time Student teaching block 2nd week of student teaching End of student teaching

Min

Max

µ

σ

4.88 5.08 5.83

7.88 9.00 8.92

6.23 7.11 7.30

0.78 1.22 0.99

Note. 1 = Nothing, 3= Very Little, 5= Some Influence, 7= Quite a Bit, 9 = A Great Deal

184

The researchers sought to describe teacher self-efficacy in three domains (efficacy for instructional strategies, efficacy for classroom management, and efficacy for student engagement) at three different points during the teacher education program (see Table 2, Table 3, & Table 4). Teacher candidates experienced the least change in the student engagement domain from the first assessment to the last assessment, and the largest change in the classroom management domain. In the student engagement domain, candidates reported the lowest teacher self-efficacy levels (µ = 6.24) at the conclusion of the student teaching block, with a range from 4.88 to 7.63. Candidates reported the highest teacher self-efficacy levels (µ = 7.14), with scores ranging from 5.38 to 9.0 at the conclusion of the student teaching internship. The range during the second week of student teaching was slightly larger, with scores ranging from 4.38 to 9.00 and an average of 6.76. The scores in the student engagement domain continued to rise throughout the teacher preparation experience, while scores in the other two domains increased between the student teaching block and the second week of the student teaching experience, and then decreased slightly at the end of the student teaching internship. Table 2 Teacher candidate (N=24) perceptions of teacher self-efficacy in the student engagement domain Time

Min

Max

µ

σ

Student teaching block

4.88

7.63

6.24

0.71

2nd week of student teaching

4.38

9.00

6.76

1.41

End of student teaching

5.38

9.00

7.14

1.05

Note. 1 = Nothing, 3= Very Little, 5= Some Influence, 7= Quite a Bit, 9 = A Great Deal In the classroom management domain, candidates reported the highest levels (µ = 7.40), of teacher self-efficacy during the second week of the student teaching internship. Candidates were the least efficacious in the classroom management domain at the conclusion of the student teaching block (µ = 6.18), with scores ranging from 4.88 to 8.00. The candidates’ sense of teacher self-efficacy decreased at the end of the student teaching experience (µ = 7.38), with scores ranging from 5.13 to 9.00. Table 3 Teacher candidate (N=24) perceptions of teacher self-efficacy in the classroom management domain Time Student teaching block

Min

Max

µ

σ

4.88

8.00

6.18

0.88

185

2nd week of student teaching

5.63

9.00

7.40

1.18

End of student teaching

5.13

9.00

7.38

1.11

Note. 1 = Nothing, 3= Very Little, 5= Some Influence, 7= Quite a Bit, 9 = A Great Deal Candidates were the most efficacious in the instructional strategies domain during the second week of their student teaching internship (µ = 7.44), with a range of 5.63 to 8.00, and the least efficacious at the end of the student teaching block (µ = 6.29), with scores ranging from 4.63 to 8.0. The candidates’ sense of teacher self-efficacy decreased at the end of the student teaching internship to an average of 7.37, with a range from 5.88 to 9.0. Table 4 Teacher candidate (N=24) perceptions of teacher self-efficacy in the instructional strategies domain Time

Min

Max

µ

σ

Student teaching block

4.63

8.00

6.29

0.93

2nd week of student teaching

5.63

8.00

7.44

1.15

End of student teaching

5.88

9.00

7.37

1.00

Note. 1 = Nothing, 3= Very Little, 5= Some Influence, 7= Quite a Bit, 9 = A Great Deal Figure 1 illustrates the change in overall teacher self-efficacy and within each of the three underlying domains. The only teacher self-efficacy domain that produced increased scores over the three data collection periods was the student engagement domain; however, this domain also had the lowest summated mean in the final two assessments. The classroom management domain and the instructional strategies domain both evidenced an increase in scores during the second week of the student teaching internship, but then decreased slightly at the end of the student teaching internship.

186

Changes in Teacher Self-Efficacy

7.4

7.38

6.76

7.14

7.44

7.37

7.11

7.3

Classroom Management

6.18 Student Engagement

6.24 6.29 6.23

1

2

Instructional Strategies

Overall Teacher Self-Efficacy

3

Figure 1. Changes in teacher self-efficacy (1= Student Teaching Block, 2= 2nd week of student teaching, 3= End of student teaching) The second research objective was to describe teacher candidates’ perceptions of their preparation at the beginning and at the end of their student teaching internship. The candidates’ perceptions of their preparation remained consistent throughout the student teaching internship (Table 5). The respondents indicated that they were the least prepared in the student engagement domain, and were the most prepared in the classroom management and instructional strategies domain. Table 5 Teacher candidate (N=24) perceptions of their preparation Domain 2nd week of student teaching σ µ

End of student teaching µ σ

Student engagement

6.20

1.02

6.22

1.02

Classroom management

6.72

1.30

6.76

1.25

Instructional strategies

6.78

1.05

6.72

1.06

Overall

6.55

0.96

6.56

1.05

Note. 1= Not Prepared, 3= Slightly Prepared, 5= Fairly Well Prepared, 7= Well Prepared, 9 = Very Well Prepared The third research question was to describe the discrepancy between teacher candidates’ reported levels of teacher self-efficacy and their perceptions of their preparation. The discrepancy score was used to determine if the candidates’ levels of teacher self-efficacy were 187

equivalent to their perceptions of their preparation. The overall discrepancy was greatest at the conclusion of the student teaching internship. The discrepancy in the student engagement domain increased between the second week of the student teaching internship and the end of the internship. The classroom management and instructional strategies domains remained fairly stable throughout the student teaching internship. Table 6 Discrepancy between teacher candidate (N=24) perceptions of teacher self-efficacy and level of preparation in three domains of teacher self-efficacy. Domain 2nd week of student teaching End of student teaching Discrepancy Discrepancy Student engagement

0.56

0.92

Classroom management

0.68

0.62

Instructional strategies

0.66

0.65

Overall

0.56

0.74

Conclusions/Recommendations/Implications The researchers sought to assess agricultural education teacher candidates’ levels of teacher self-efficacy and their perceptions of their preparation. The agricultural education teacher candidates’ sense of teacher self-efficacy changed throughout the pre-service teacher education program. Although the timing of the assessments differed from previous studies, the observed changes in teacher self-efficacy corroborated previously-reported research (Knobloch, 2006; Knobloch & Whittington, 2003; Roberts, Harlin, & Ricketts, 2006). Specifically, Roberts, Harlin, and Ricketts (2006) suggested that there was a need to investigate teacher self-efficacy in multiple teacher candidate populations, and to determine if those populations had similar changes in teacher self-efficacy, and were the most efficacious in the instructional strategies domain, and least efficacious in the student engagement domain. The results of this study support the findings of Roberts, Harlin, and Ricketts (2006), as agricultural education teacher candidates in this study reported the lowest levels of teacher self-efficacy in the student engagement domain, and the highest scores in the instructional strategies domain. Teacher candidates were not assessed in the middle of the student teaching experience, so it cannot be determined if they experienced a decline in teacher self-efficacy in the middle of the student teaching internship. Agricultural education teacher candidates revealed the lowest overall sense of teacher self-efficacy during the student teaching block, and the highest overall sense of teacher selfefficacy at the end of the student teaching internship. Based on these findings, the researchers concluded that the student teaching internship increased the candidates’ sense of teacher selfefficacy. Knobloch (2006) speculated that teacher candidates “. . . may feel that they already know how to teach before their student teaching experience” (p. 45). However, the lower teacher 188

self-efficacy scores reported at the end of the student teaching block seem to contradict Knobloch’s (2006) conclusion. Conversely, candidates may be somewhat overwhelmed at the end of their teacher preparation course work, which may contribute to their lower levels of teacher self-efficacy at that time. In the student engagement domain, candidates had the lowest levels of teacher selfefficacy during two of the three assessment periods. The intensive preparation during the student teaching block and the experiences during the student teaching internship do not appear to improve candidate perceptions of teacher self-efficacy in the student engagement domain when compared to the classroom management domain and the instructional strategies domain. Roberts, Harlin, and Ricketts (2006) noted that teacher self-efficacy scores in the student engagement domain may be slightly lower than in the other domains due to “. . . the complex nature of interacting and connecting with diverse youth, coupled with a novice teacher’s attention to the mechanics of instruction and classroom management” (p. 90). However, since the student engagement domain produced the least amount of positive change in teacher selfefficacy, increased assistance in this area throughout the teacher preparation program may be warranted. Early field experiences in which teacher education candidates can practice and hone their skills in respect to the student engagement domain may improve the candidates’ perceptions of teacher self-efficacy in this domain. The incorporation of the Borich (1980) needs assessment model identified areas where candidates may need additional assistance or professional development. Candidate perceptions of their preparation remained consistent throughout the student teaching experience. The student engagement domain had notably lower preparation scores than the other domains. Based on this finding, the researchers concluded that candidates may require more assistance in the student engagement domain prior to the student teaching internship. The researchers sought to describe the discrepancy between candidates’ teacher selfefficacy and their perceptions of their preparation. Because the candidate’s perception of their preparation was the same at the beginning of the student teaching internship as it was at the end of the internship, the discrepancy score increased as the candidates levels of teacher self-efficacy increased. Candidate perceptions of preparation, while lower than the respective levels of teacher self-efficacy do not appear to change throughout the student teaching internship. Further research should examine why student teachers rate their preparation lower than their teacher selfefficacy. This study, although replicating previous studies tracking the changes of teacher selfefficacy in agricultural education, adds to the knowledge base of teacher self-efficacy in agricultural education by assessing candidates’ perceptions of their preparation. The lower levels of preparation reported in the student engagement domain are troubling. Candidates may not feel adequately prepared to influence student achievement by engaging them in the learning process, resulting in a lower teacher self-efficacy score in this domain. Further research should be conducted in an effort to improve teacher candidates’ sense of teacher self-efficacy in the student engagement domain.

189

References Bandura, A. (1986). Social foundations of thought and action- A social cognitive theory. New Jersey- Prentice Hall. Bandura, A. (1994). Self-efficacy. In V.S. Ramachaudran (Ed.), Encyclopedia of human behavior (Vol. 4, pp. 71-81). New York- Academic Press. Bandura, A. (1997). Self-efficacy- The exercise of control. New York- W.H. Freeman and Company. Borich, G. D. (1980). A needs assessment model for conducting follow-up studies. Journal of Teacher Education, 31(3), 39-42. Darling-Hammond, L., Chung, R., & Frelow, F. (2002). Variation in teacher preparation: How well do different pathways prepare teachers to teach? Journal of Teacher Education, 53(4), 286-302. Harlin, J. F., Roberts, T. G., Briers, G. E., Mowen, D. L., & Edgar, D. W. (2007). A longitudinal examination of teaching efficacy of agricultural science student teachers at four different instititions. Journal of Agricultural Education, 48(3), 78-90. Kantrovich, A. J. (2007, May). A national study of the supply and demand for teachers of agricultural education from 2004-2006. American Association for Agricultural Education. Retrieved June 19, 2007, from http-//aaae.okstate.edu/. Knobloch, N. A. (2001). The influence of peer teaching and early field experiences on teaching efficacy beliefs of preservice educators in agriculture. Proceedings of the National Agricultural Education Research Conference, New Orleans, LA, 28, 119-131. Knobloch, N. A. (2006). Exploring relationships of teachers’ sense of efficacy in two student teaching programs. Journal of Agricultural Education, 47(2), 36-47. Knobloch, N. A., & Whittington, M. S. (2002). Novice teachers’ perceptions of support, teacher preparation quality, and student teaching experience related to teacher efficacy. Journal of Vocational Education Research, 27(3), 331-341. Knobloch, N. A., & Whittington, M. S. (2003). The influence of the initial ten weeks of the school year on novice teacher efficacy in Agricultural Education. NACTA Journal, 47(4), 16-21. Pajares (2002). Overview of social cognitive theory and of self-efficacy. Retrieved May 15, 2007, from http-//www.emory.edu/EDUCATION/mfp/eff.html.

190

Roberts, T. G., Harlin, J. F., & Ricketts, J. C. (2006). A longitudinal examination of teaching efficacy of agricultural science student teachers. Journal of Agricultural Education, 47(2), 81-92. Rocca, S. J., & Washburn, S. G. (2006). Comparison of teacher efficacy among traditionally and alternatively certified agricultural teachers. Journal of Agricultural Education, 47(3), 5869. Rubeck, M., & Enochs, L. (1991). A path analysis model of variables that influence science and chemistry teaching self-efficacy and outcome expectancy in middle school science teachers. Paper presented at the annual meeting of the National Association for Research in Science Teaching, Lake Geneva, WI. Team AgEd. (2007). 2005-2006 Annual Report on Agricultural Education. Tschannen-Moran, M., Woolfolk Hoy, A., Hoy, W. K. (1998). Teacher efficacy: Its meaning and measure. Review of Educational Research, 68, 202-248. Tschannen-Moran, M., & Woolfolk Hoy, A. (2001). Teacher efficacy: Capturing an elusive construct. Teaching and Teacher Education, 17, 783-805. Whittington, M. S., McConnell, E., & Knobloch, N. A. (2006). Teacher efficacy of novice teachers in agricultural education in Ohio at the end of the school year. Journal of Agricultural Education, 47(2), 26-38. Woolfolk Hoy, A. E., & Hoy, W. K. (2009). Instructional leadership: A research-based guide to learning in schools. Boston, MA: Allyn and Bacon. Authors Kattlyn J. Wolf is an Assistant Professor in the Department of Agricultural and Extension Education at the University of Idaho, 1134 W. Sixth St, Moscow, ID 83844. Email: [email protected]. Phone: 208-885-6358. Daniel D. Foster is a Graduate Associate in the Department of Human and Community Resource Development at The Ohio State University, 208 Agricultural Administration, 2120 Fyffe Road, Columbus, OH 43210. Email: [email protected]. Phone: 614-292-6909. Fax: 614-292-7007. Robert J. Birkehnolz is a Professor and Chair in the Department of Human and Community Resource Development at The Ohio State University, 208 Agricultural Administration, 2120 Fyffe Road, Columbus, OH 43210. Email: [email protected]. Phone: 614-292-6909. Fax: 614-292-7007.

191

ARE THEY SATISFIED? USING AGRICULTURAL EDUCATION GRADUATES’ GEFT SCORES TO ASSESS JOB SATISFACTION J. Shane Robinson, Oklahoma State University Tracy Kitchel, University of Kentucky Bryan L. Garton, University of Missouri Abstract The purpose of this study was to assess satisfaction variables (salary, academic advising, career satisfaction, and overall program quality) of agricultural education graduates at the University of Missouri according to their learning style. The Group Embedded Figures Test (GEFT) was used to measure learning style. The results of the study revealed the overall mean GEFT score for the graduates were 12.9, indicating the group were more field-independent than fielddependent. Over two-thirds (68.93%) of the graduates were identified as field-independent. No practical differences existed in employment decisions between those who were field-independent and those who were field-dependent. When job satisfaction scores were correlated with GEFT scores, a positive, low correlation existed (r = .11), indicating that GEFT was not a good predictor of job satisfaction, even though it had been linked with academic performance and overall success in higher education (Cano, 1999; Cano & Porter, 1997; Garton, Dauve, & Thompson, 1999; Torres, 1993; Torres & Cano, 1994). When aspects of academic advising mean scores were compared by learning style, little differences existed. In all, when compared by learning style, little differences existed in current employment, salary, academic advising, overall program quality, and job satisfaction. Introduction – Theoretical Framework Not all graduates enter the exact professions in which they were prepared. Likewise, not all agricultural education graduates enter the teaching profession. In a study of agricultural education graduates at the University of Missouri, Cartmell and Garton (2000) found over onethird had entered professions outside of teaching. With graduates entering non-teaching jobs, agricultural education programs must be able to prepare students for a variety of careers. Specifically, the agricultural education curriculum should address the educational and career preparation needs of students who desire careers outside of school-based teaching (Goecker, 1992) because when students are equipped for a variety of careers, the preparation is reflected well upon the university. However, not all graduates feel prepared once they graduate. According to Candy and Crebert (1991), graduates sometimes struggle because they are unfamiliar with how to cope in a new environment. Graduates fail to adjust to the lack of a structured environment such as those provided in higher education settings. Because graduates struggle to adjust to their new environment, it becomes increasingly important for universities to track their graduates, know where they go, and what becomes of them in their future endeavors. It also becomes important to identify factors, within the control of the university, that contribute to preparing students for successful careers. Martin, Milne-Home, Barrett, Spalding, and Jones (2000) concluded that identifying such factors could better prepare graduates for their chosen

192

careers and meet the needs of employers. However, the task of improving these factors becomes more difficult when a program offers a variety of career options. A possible factor to consider is job satisfaction. Job satisfaction could be viewed as a determinant for the retention of graduates in their chosen career. “Job satisfaction refers to the individual’s attitude toward the various aspects of their job as well as the job in general” (Rogers, Clow & Kash, 1994, p. 15). For graduates to maximize their performance on the job, they must be satisfied with their job. Tse & Wilton (1988) stated in order for people to experience satisfaction on the job, they must perceive themselves as performing successfully. Given the context of the university setting, the way a person performs or learns could be used to predict their job satisfaction. Pace (1987; cited in Martin et al. 2000) noted that “perceptions of learning . . . were related to college satisfaction” (p. 201). If a student’s perception of learning relates to being satisfied in college, can learning style be used to predict one’s career satisfaction? Lovelace (2005) stated that “learning style is the way that students begin to concentrate on, process, internalize, and remember new and difficult academic information” (p. 176-177). Learning style has been explained as distinct behaviors which serve as stable indicators of how a person learns and adapts to his/her learning environment (Gregorc, 1979). It has also been identified as a factor influencing how students transition from school to work. Candy and Crebert (1991) noted a disparity between how a university prepares a student for work and how the workplace utilizes that employee’s learning style. One form of measuring one’s learning style is the group embedded figures test (GEFT). An extensive amount of research in agricultural education has linked learning style to fielddependence/independence (Guild & Garger, 1985; Witkin, Oltman, Raskin & Karp 1971) GEFT test. Individuals who prefer a field-dependent learning style tend to have a global perception, struggle to solve problems, are more attuned to their social environment, learn better when concepts are humanized, and favor a spectator approach to learning. Additionally, fielddependent learners tend to be more extrinsically motivated and learn better when organization and structure is provided by the teacher (Witkin, Moore, Goodenough, & Cox, 1977). Conversely, individuals who prefer a field-independent learning style tend to view concepts more analytically, and find it easier to solve problems. They also tend to favor learning activities that require individual effort and study. Additionally, field-independent learners prefer to develop their own structure and organization for learning, are intrinsically motivated, and are less receptive to social reinforcement (Witkin et al., 1977). In a study of Ohio State University majors, Kitchel and Cano (2001) found that 64% were field-independent. Hughes (1937) posited that for success and satisfaction to occur in one’s job, both objective and subjective criteria must be present. Heslin (2005) noted that objective career success entails pay and promotions while subjective career success entails job satisfaction, earnings, and job status. Kaskiri (2006) stated that success related to one’s career is based upon criteria such as salary and level of job satisfaction as well as predictors such as cognitive ability,

193

socio-economic status, and personality factors. To that end, can one’s GEFT score be used to predict job satisfaction? While learning styles (e.g. GEFT scores) have been found to have a positive relationship with academic performance, as measured by grade point average (Torres, 1993; Torres & Cano, 1994), performance in agriculture courses (Garton, Dauve, & Thompson, 1999), and overall success in higher education (Cano & Porter, 1997; Cano, 1999), there have been no studies that have sought to determine if relationships exist between GEFT score (learning style) and career satisfaction of agricultural education graduates. However, the claim seems plausible. Vangsnes (2007) stated It has been shown . . . that individuals in different career fields exhibit characteristics of learning that seem to correlate with job responsibilities. What has not been discussed is a possible relationship between vocational satisfaction in relationship to preferred learning style (p. 66). In fact, Vangsnes suggested that a “person’s satisfaction with his/her job, has to do with the way people learn, or their learning style” (p.1). Vangsness further posited “If people pursue their desired field of study based upon their learning style, then it is reasonable to assume they will also exhibit more vocation/career satisfaction than those individuals who have not” (p. 66). Therefore, the central foci driving this study were twofold: to examine if and where the relationships between graduate satisfaction and their learning style existed and to determine what implications graduates’ learning style had upon their career choice. Purpose and Objectives The purpose of this study was to compare the career satisfaction variables (salary, academic advising, career satisfaction and overall program quality) of agricultural education graduates at the University of Missouri according to their GEFT score. The following objectives were formulated for the study: 1. Describe the salary and GEFT scores of the population. 2. Compare graduates on their current employment decision by their GEFT scores. 3. Compare graduates’ salaries, perceptions of the academic advising they received, and their views about the overall program quality by their GEFT scores. 4. Compare graduates’ level of career satisfaction by their learning style and determine if a relationship exists between their perceptions of career satisfaction and GEFT scores. Methods This research was descriptive-correlational in nature and consisted of a five-year census of agricultural education graduates (N = 112) from the University of Missouri. Students enrolled in agricultural education at this institution choose between two degree options: teacher 194

certification and leadership. Those who choose the teacher certification option acquire a teaching license and develop skills to teach agriculture in school-based settings, while those in the leadership option develop and apply their leadership, communication, and human relation skills to careers in industry by planning, managing, and disseminating information in non-formal educational settings. In all, a total of 96 graduates responded for an 86% response rate. In particular, the population for this study consisted of the same group used in a related study by (Garton & Robinson, 2006). As to avoid duplication of the findings, yet properly describe the context of the sample, the following demographic data of graduates are provided: 86% were employed full-time. Of these full-time graduates, 39% were employed as secondary public school teachers, and the remaining 61% of graduates were employed in various industry positions such as sales, management, and communications to name a few. For the purpose of this study, two parallel questionnaires were developed: one for graduates who pursued careers in industry and one for graduates who pursued teaching schoolbased agriculture. The questionnaires consisted of seven sections: occupational status, current job satisfaction, factors influencing occupational change, educational experiences, program assessment, quality of academic advising, and open-ended questions. The Brayfield-Rothe (1951) job satisfaction instrument, as modified by Warner (1973), was included for collecting data pertaining to this study in section one. This section consisted of job satisfaction and dissatisfaction factors and used a five-point Likert scale consisting of: 1 = strongly disagree, 2 = disagree, 3 = undecided, 4 = agree and 5 = strongly agree. The researchers developed the remaining six sections of the questionnaire. Agricultural education faculty and university career placement personnel served as the panel of experts and established the content and face validity of the instruments. Reliability for the job satisfaction section was established through prior research with secondary agriculture teachers. Cano and Miller (1992) reported a Cronbach’s alpha coefficient of .94 for the summated scale. Reliability for the remaining sections was established through a pilot test with 16 senior agricultural education students. Spearman-Brown split-half reliability coefficients ranged from .82 for the quality of academic advising section to .69 for the educational experiences section. The Group Embedded Figures Test (GEFT) (Witkin et al., 1971) was administered during the graduates’ undergraduate program to assess the preferred learning style of students as fielddependent or field-independent. The possible range of scores on the GEFT is zero to 18. Individuals scoring a 0-11 were considered to prefer a field-dependent learning style, while individuals scoring 12-18 were considered to prefer a field-independent learning style. The GEFT is a standardized instrument that has been used in educational research for more than 30 years (Guild & Garger, 1985). The validity and reliability of the GEFT was established by the developers of the instrument. The GEFT is a timed test; therefore, internal consistency was measured by treating each section as split halves (r = .82) (Witkin et al., 1971). Descriptive statistics (means, frequencies, percentages, and standard deviations) were used to analyze the data. A Pearson-product moment correlation was used, for objective five, in an effort to describe the relationship between career satisfaction and learning style. 195

Findings Objective one sought to describe the salary and GEFT scores of the population. A comparison of salaries revealed that only one graduate with a public school teaching career earned less than $20,000, while 10 graduates with industry careers earned less than $20,000 (Table 1). Likewise, none of the public school teaching graduates earned $50,000 or greater; however, nine graduates in industry positions earned $50,000 per year or more. Table 1 Salary Comparison of Graduates in Secondary Teaching vs. Industry Careers Public School Teaching Industry Position Salary % % f f Less than $20,000 1 2.7 10 17.9 $20,000 – 24,999 1 2.7 5 8.9 $25,000 – 29,999 3 8.1 8 14.3 $30,000 – 34,999 13 35.1 7 12.5 $35,000 – 39,999 15 40.5 8 14.3 $40,000 – 44,999 3 8.1 4 7.1 $45,000 – 49,999 1 2.7 5 8.9 $50,000 or greater 0 0 9 16.1 Total 37 100.0 56 100.0 An analysis of GEFT scores revealed a mean score of 12.88 (SD = 3.89), indicating that the group was more field-independent than field-dependent (Figure 1). The most frequent score was 15 (n = 19), followed by scores of 14 and 18 (n = 11) for each; thus, it was found that 32 (31%) of those who completed the GEFT were field-dependent and 71 (69%) were field-independent.

Figure 1. Distribution of GEFT learning style scores. 196

In meeting objective 2, which was to compare graduates on their current employment decision by their learning style, Table 2 was constructed and sorted by difference in percent from highest to lowest. When comparing percentage break-outs by employment decision, the highest percentage difference between those who were field-dependent and those who were fieldindependent was in “sales” (difference = 7.79%) and “public school teaching” (difference = 7.46%) as their current employment. Table 2 Current Employment Decisions Compared By GEFT Scores Field-Dependent Employment Decision % f Sales 6 20.69 Public School Teaching 10 34.48 Communications 1 3.45 Education/Training (non-school) 1 3.45 Government Agencies 2 6.90 Management 3 10.34 Other 2 6.90 Graduate School 2 6.90 Production Agriculture 1 3.45 Financial Services 1 3.45 Total 29 100.00

Field-Independent % f 8 12.90 26 41.94 4 6.45 4 6.45 1 1.61 8 12.90 3 4.84 4 6.45 2 3.23 2 3.23 62 100.00

Differences 7.79 7.46 3.00 3.00 5.29 2.56 2.06 0.45 0.22 0.22

30.00% 25.00% 20.00% 15.00% 10.00% 5.00% 0.00%

Field Dependent

$2 $2 0, 0, 00 00 0 0 – $2 24 5, ,9 00 99 0 – $3 29 0, ,9 00 99 0 – $3 34 5, ,9 00 99 0 – $4 39 0, ,9 00 99 0 – $4 44 5, ,9 00 99 0 – $5 49 0, ,9 00 99 0 or gr ea te r

Field Independent

Le ss

th an

Percentage

Objective three sought to compare graduates’ salary, academic advising, and overall program quality by their GEFT scores. An examination of the distribution revealed that the pattern appears similar (Figure 2).

Salary Range

Figure2. Distribution of salary by GEFT scores.

197

The biggest discrepancy occurred at $50,000 or more. Nearly 15% of field-dependent graduates made $50,000 or more as compared to roughly 7% of field-independent graduates. In addition, roughly 14% of field-independent learners made less that $20,000 as compared to 11% of field-dependent graduates. Table 3 compared academic advising mean scores by GEFT scores and was sorted by differences in mean scores from highest to lowest. Differences in the academic advising mean scores by learning style ranged from .03 to .25. Six academic advising items had a mean score difference above .10 while four items had mean score differences below .10. The largest mean score difference was for the item “planning courses” (difference = .25), and “Organization – Records” (difference = .19), “degree requirements” (difference =. 17), “meeting availability” (difference = .13) and “academic excellence” (difference = .12) followed respectively. “Career advising” (difference = .03) had the smallest mean score difference. As a whole, both fielddependent and field-independent graduates were most satisfied with their academic advisor’s ability to prepare them for their degree’s requirements (M f-d = 4.59, M f-i = 4.42). Likewise, both field-dependent and field-independent graduates were least satisfied with the academic advising item “experiences – career preparation” (M f-d = 3.48, M f-i = 3.53). Table 3 Academic Advising Mean Scores Compared by GEFT Scores Field-Dependent Field-Independent Academic Advising Items M SD M SD Planning Courses 4.41 .73 4.16 .91 Organization - Records 4.48 .51 4.29 .88 Degree Requirements 4.59 .57 4.42 .84 Meeting Availability 4.45 .69 4.32 .84 Academic Excellence 4.31 .71 4.19 .90 Academic Progress 4.21 .73 4.10 .96 Adequate Time 4.41 .73 4.35 .87 Respect – Value Opinion 4.28 .96 4.34 .85 Experiences – Career Preparation 3.48 .99 3.53 1.13 Career Advising 3.69 1.04 3.66 1.07 Note. Scale: 1 = Poor, 2 = Fair, 3 = Satisfactory, 4 = Very Good, 5 = Excellent

Differences .25 .19 .17 .13 .12 .11 .06 .06 .05 .03

Table 4 compared overall program quality mean scores by GEFT scores and was sorted by differences in mean scores from highest to lowest. Eight overall program quality items had a mean score difference above .10 while six items had mean score differences below .10. The largest mean score difference was with the item “job placement” (difference = .28). The second highest was a difference of .27 with the item “student organizations.” “Internships” (difference = .25), “quality of students” (difference = .20) and “support since graduation” (difference = .17) rounded out the top five. Both field-dependent and field-independent learners scored “ag ed facilities” (difference = 2.97) exactly the same. Table 4 Overall Program Quality Item Mean Scores as Compared by GEFT Scores 198

Field-Dependent Overall Program Quality Items M SD Job Placement 2.68 .90 Student Organizations 3.82 .39 Internships 3.67 .62 Quality of Students 3.72 .45 Support Since Graduation 3.11 .83 Instruction 3.76 .44 Computer Support 3.04 .88 Availability of Ag Ed Courses 3.59 .50 Curriculum Organization 3.62 .56 Availability of Required Courses 3.11 .74 Courses Preparing for Employment 3.34 .55 Faculty Competence 3.76 .44 Courses Preparing for Grad School 3.47 .64 Ag Ed Facilities 2.97 .73 Note. Scale: 1 = Poor, 2 = Fair, 3 = Good, 4 = Excellent

Field-Independent M SD 2.96 .89 3.55 .70 3.42 .88 3.52 .54 2.94 .94 3.63 .49 2.93 .79 3.69 .53 3.71 .49 3.18 .59 3.40 .59 3.73 .45 3.46 .69 2.97 .79

Differences .28 .27 .25 .20 .17 .13 .11 .10 .09 .07 .06 .03 .01 .00

Objective four sought to compare graduates’ level of career satisfaction by their GEFT scores and determine if a relationship existed between career satisfaction and learning style. Career satisfaction mean scores differed by .05 between field-dependent and field-independent learners (Table 5). A low positive Pearson-product moment correlation of .11 was found between overall job satisfaction and GEFT scores (Davis, 1971). Table 5 Overall Career Satisfaction Mean Scores by GEFT Scores Field-Dependent Variable SD M Overall Job Satisfaction 4.12 .41 Note. r = .11; Scale: 1 = Strongly Disagree to 5 = Strongly Agree

Field-Independent SD M 4.17 .45

Conclusions/Recommendations/Implications With regard to salaries, graduates in school-based teaching positions were more similar as opposed to those with industry careers. Approximately 75% of the school-based teachers earned a salary in the range of $30,000 to $39,999. While some industry professionals started at lower salaries as compared to school-based teachers, there is no ceiling as to the salary an industry professional can make. Of these graduates, the overall mean GEFT score was 12.9, indicating the group leaned toward being more field-independent than field-dependent. Over two-thirds (69%) were identified as field-independent, meaning the group as a whole tended to be more analytical and independent in its learning preference (Witkin et al., 1977). This finding is consistent with the findings of Kitchel and Cano (2001), who found that 64% of agricultural education majors were field-independent.

199

While both field-dependent and field-independent learners were equally satisfied with their chosen career, the graduates in this study with the highest salaries were predominately field-dependent. Specifically, a higher percentage of graduates entering sales type positions were more field-dependent, while those teaching in public schools were predominately fieldindependent. Little differences existed when comparing aspects of academic advising mean scores by GEFT score. The item “help in planning courses for degree program” had the highest amount of discrepancy, while “quality and availability of job placement” held the highest mean score difference between learning styles on overall program quality. Overall, graduates tended to be very positive toward the advising they received regardless of learning style. Job satisfaction mean scores were calculated and correlated with GEFT scores. A positive, low correlation resulted, indicating that GEFT was not a good predictor of job satisfaction even though it had previously been linked with academic performance and overall success in higher education (Cano, 1999; Cano & Porter, 1997; Garton, Dauve, & Thompson, 1999; Torres, 1993; Torres & Cano, 1994). Implications One could imply the reason more field-dependent learners are entering sales positions and earning greater salaries is due to the fact that these individuals are more extrinsically motivated. Maybe these individuals have recognized and applied their strengths and preferred learning styles in the workforce. If so, perhaps this finding supports Vangsnes’s (2007) assumption that “If people purse their desired field of study based upon their learning style, then it is reasonable to assume they will also exhibit more vocation/career satisfaction than those individuals who have not” (p. 66). Further, is it possible more field-independent learners are entering the teaching ranks as opposed to field-dependent learners because much of their job requires individual effort and study (i.e., grading papers, writing lesson plans, designing rubrics) and they like to control their own structure for the learning process which occurs in the classroom? Recommendations for Practice While little differences existed in current employment, salary, academic advising, overall program quality, and job satisfaction when compared with GEFT scores, faculty at this university can note that learning style, either randomly or programmatically, is being addressed in overall program quality and academic advising. Further, , faculty should continue to assist students in learning about their preferred learning style in an effort to assist them in gauging their performance with various courses in academia as GEFT has been associated with influencing academic performance (Cano, 1999; Cano & Porter, 1997; Garton, Dauve, & Thompson, 1999; Torres, 1993; Torres & Cano, 1994). Recommendations for Further Research

200

GEFT learning style was not a good predictor of job satisfaction. Therefore, further research on the relationship between learning styles and job satisfaction may not be warranted. However, Kaskiri (2006) noted numerous factors that could be used to predict one’s career success, such as cognitive ability, socio-economic status, and personality factors. Perhaps these factors may better explain career satisfaction that learning style. Therefore, future research should focus on these areas to determine if they are good predictors of job satisfaction. Martin et al. (2000) called for an evaluation of workplace preparation of college graduates. If learning style is not a valuable predictor, then what is? Further investigation is warranted outside of GEFT scores to identify aspects that may be significantly related to agricultural education graduates’ career satisfaction. References Brayfield, A. H., & Rothe, H. F. (1951). An index of job satisfaction [Electronic version]. Journal of Applied Psychology, 35, 305-311. Candy, P. C. & Crebert, R. G. (1991). Ivory tower to concrete jungle (the difficult transition form the academy to the workplace as learning environments) [Electronic version]. Journal of Higher Education, 62(5), 570-592. Cano, J. (1999). The relationship between learning style, academic major, and academic performance of college students [Electronic version]. Journal of Agricultural Education, 40(1), 30-37. Cano, J., & Miller, G. (1992). A gender analysis of job satisfaction, job satisfier factors, and job disatisfier factors of agricultural education teachers [Electronic version]. Journal of Agricultural Education, 33(3), 40-46. Cano J., & Porter, T. (1997). The relationship between learning styles, academic major, and academic performance of agriculture students. Proceedings of the 24th Annual National Agricultural Education Research Meeting, p. 373-380, Las Vegas, NV. Cartmell, D. D., & Garton, B. L. (2000). An assessment of agricultural education graduates’ preparation for careers in teaching and industry. Proceedings of the Twenty-Seventh National Agricultural Education Research Conference, p. 530-541, San Diego, CA. Davis, J. A. (1971). Elementary survey analysis. Englewood, NJ: Prentice-Hall. Garton, B. L., Duave, J., & Thompson, R. W. (1999). Predictors of student achievement in an introductory agricultural economics course. Proceedings of the 53rd Annual Central Region Research Conference in Agricultural Education, p. 102-108, St. Louis, MO. Garton, B. L., & Robinson, J. S. (2006). Career paths, job satisfaction, and employability skills of agricultural education graduates. North American Colleges and Teachers of Agriculture (NACTA) Journal, 50(4), 31-36.

201

Goecker, A. D. (1992). Priorities for college and university agricultural education faculty [Electronic version]. Journal of Agricultural Education, 33(1), 2-7. Gregorc, A. F. (1979). Learning/teaching styles: Potent forces behind them. Educational Leadership, 36, 234-237. Guild, P. B., & Garger, S. (1985). Marching to different drummers. Alexandria, VA: Association for Supervision and Curriculum Development. Heslin, P. A. (2005). Conceptualizing and evaluating career success [Electronic version]. Journal of Organizational Behavior, 26, 113-136. Hughes, E. C. (1937). Institutional office and the person [Electronic version]. The American Journal of Sociology, 43(3), 404-413. Kaskiri, E. A. (2006). Academic and occupational career success ten years after high school graduation. Unpublished master’s thesis, Wayne State University, Detroit. Kitchel, T., & Cano, J. (2001). The relationship between learning style and personality type of students majoring and minoring in agricultural education at The Ohio State University. Proceedings of the 55th Central States Agricultural Education Research Conference, p. 142-153, St. Louis, MO. Lovelace, M. K. (2005). Meta-analysis of experimental research based on the Dunn and Dunn model [Electronic version]. The Journal of Educational Research, 98(3), 176-183. Martin, A. J., Milne-Home, J., Barrett, J., Spalding, A., & Jones, G. (2000). Graduate satisfaction with university and perceived employment preparation [Electronic version]. Journal of Education and Work, 13(2), 199-211. Rogers, J. D., Clow, K. E., & Kash, T. J. (1994). Increasing job satisfaction of service personnel [Electronic version]. Journal of Services Marketing, 8(1), 14-26. Torres, R. M. (1993). The cognitive ability and learning style of students enrolled in the College of Agriculture at The Ohio State University. Unpublished doctoral dissertation, The Ohio State University, Columbus. Torres, R. M., & Cano, J. (1994). Learning styles of students in a college of agriculture [Electronic version]. Journal of Agricultural Education, 35(4), 61-66. Tse, D. K., & Wilton, P. C. (1988). Models of consumer satisfaction formation: An extension [Electronic version]. Journal of Marketing Research, 25(2), 204-212. Vangsnes, E. H. (2007). A comparative study of learning styles and job satisfaction to medical specialty chosen among physician assistant graduates. Unpublished doctoral dissertation, Western Michigan University: Kalamazoo. 202

Warner, P. D. (1973). A comparative study of three patterns of staffing within the cooperative extension organization and their association with organizational structure, organizational effectiveness, job satisfaction, and role conflict. Unpublished doctoral dissertation, The Ohio State University: Columbus. Witkin, H. A., Moore, C. A., Goodenough, D. R. & Cox, P. W. (1977). Field-dependent and field-independent cognitive styles and their independent cognitive styles and their educational implications. Review of Educational Research, 47(1) 1-64. Witkin, H. A., Oltman, P.K., Raskin, E., & Karp, S.A. (1971). Group Embedded Figures Test Manual. Palo Alto, CA: Consulting Psychologist Press. Authors J. Shane Robinson is an Assistant Professor in the Department of Agricultural Education, Communications and Leadership at Oklahoma State University, 440 Ag Hall, Stillwater, OK 74078-6032. Email: [email protected]. Phone: 405-744-3094. Fax: 405-744-5176. Tracy Kitchel is an Assistant Professor in the Department of Community and Leadership Development at the University of Kentucky, 713 W. P. Garrigus Building, Lexington, KY 40546. Email: [email protected]. Phone: 859-257-4273. Fax: 859-257-1164. Bryan L. Garton is a Professor in the Department of Agricultural Education and Interim Associate Dean of Academic Programs in the College of Agriculture, Food and Natural Resources at the University of Missouri, 123 Gentry Hall, Columbia, MO 65211. Email: [email protected]. Phone: 573-882-7451. Fax: 573-884-4444.

203

PREDICTORS OF JOB STRESS AMONG MISSOURI SECONDARY AGRICULTURAL EDUCATION TEACHERS Robert M. Torres, University of Missouri Misty D. Lambert, University of Missouri Rebecca G. Lawver, University of Missouri Abstract The study sought to describe the demographic characteristics and explain the current level of job stress among secondary agriculture teachers in Missouri. The sample consisted of secondary agriculture teachers (n=252). Data were collected using the Job Stress Survey (Spielberger & Vagg, 1999). From the findings it was concluded that the average secondary agriculture teacher was a male with over 11 years of experience. Almost half work in a single teacher department and reported working between 46-65 hours per week. The majority of teachers also reported that they received social support from friends, family, professional associations and community organizations. The number of hours per week at work was the largest predictor on each index scale used to measure job stress. The second predictor on the Lack of Support Index scale was years of service at the current school. The second predictor of both Job Pressure Index and Job Stress Index scores was the total years of teaching experience, suggesting that the longer teachers stay in the profession or at their current school, the less stressed they tend to be. Introduction There are a variety of definitions of stress, from very simple to complex. Humphrey and Humphrey (1986) defined stress as “any factor acting internally or externally that makes it difficult to adapt and that demands increased effort from the person to maintain a state of equilibrium within himself and his external environment” (p. 2-3). According to the American Psychological Association (2007), one-third of people in the U.S. regularly report experiencing extreme levels of stress and nearly one in five reports that they experienced their highest level of stress 15 or more days per month. Concern with the effects of job stress on productivity, absenteeism, and health-related problems has increased dramatically during the last decade (Vagg & Spielberger, 1998). The most influential theory for conducting research on job stress has been PersonEnvironment (PE) fit theory (Brewer & McMahan, 2004, Edwards & Cooper, 1990, Spielberger & Vagg, 1999). The PE fit theory is proposed as an approach for understanding the process of adjustment between individuals and their work environment (Caplan, 1987). According to this theory, stress and strain in the workplace result from the interaction of an individual with his or her work environment (Vagg & Spielberger, 1998). The interaction between individual and environment determines whether or not a situation is stressful (Brewer & McMahan, 2004). When demands of the job exceed a person’s ability to meet those demands, the fit between an individual and their environment is incompatible.

204

PE fit theory consists of two basic characteristics regarding a person and the environment. The first measurement is objective and the second is subjective. The objective environment indicates physical and social situations and events as they exist, independent of the person’s perceptions, whereas the subjective environment refers to situations and events as perceived by the person (Edwards & Rothbard, 1999). This study focuses on subjective fit measures of PE because this study is concerned with perceptions of job stress. Review of Literature Most people think that stress is a result of a key life event; a major illness, a death in the family, or the loss of a job. However, they tend to ignore the little insults of everyday life (London & Spielberger, 1983). Job stressors include factors such as work conditions, technological advancements, work responsibilities, underutilization, lack of autonomy, role conflict, lack of support from supervisors and colleagues, the organizational climate and transferable job skills (Cooper & Payne, 1988). Specifically, among teachers, it is not surprising that 23 percent of teachers believe that they have a poor ability to cope with stress (Humphrey & Humphrey, 1986). Our educational system has undergone vast and rapid changes, including the introduction of the No Child Left Behind Act. While most teachers agree that teaching is rewarding, it is a difficult career because of too few resources, too much paperwork, crowded classrooms, students with emotional problems, low salary and high-stakes standardized testing (Strauss, 2002). The variety of stressors for secondary teachers is clear. However, stress among teachers is not simply exposure to these sources of difficulty, but can vary due to psychological and social support (Griffith, Steptoe & Cropley, 1999). The personal characteristics of teachers include personality, ability, physical and demographic traits. These combine with the stressors in the environment to produce strain in the person (Cooper, 1998). Secondary agriculture teachers face even greater job demands than non-career and technical education teacher as they often work well beyond a 40-hour work week to supervise student projects, coach career development teams, evaluate student work and prepare lessons (Croom, 2003; Straquadine, 1990). Other factors influencing stress include personality as it makes a significant contribution to the performance and well-being of the individual (Kenny, 1999). Further, research suggests that personal attributes such as gender can also influence work stress, identifying women as experiencing an overall greater amount of work-related stress (Piltch, Walsh, Mangione, & Jennings, 1994, Spielberger & Reheiser, 1995, Bhatnagar, 1988 & Gadzella, Ginther, Tomcala, & Bryant, 1991). The end result of teacher stress is that many talented men and women with high expectations of achievement are dispirited and disillusioned. Some leave the teaching profession; others stay, but are plagued by a multitude of physical, emotional and behavioral stress-related manifestations (Milstein & Golaszewski, 1985). This is particularly true for new teachers. Roulston, Legette, and Womack (2005) confirm that about thirty-three percent of new teachers quit the teaching profession within the first three years of their career. Having the ability to deal with stress is vital in teacher retention. According to Croom (2003), agriculture teachers experience moderate levels of emotional exhaustion in their work.

205

The demands of the job coupled with the range of responsibilities of operating, managing and teaching in an agricultural education department may well create stress in teachers. Little problems do add up; taking more of a toll on the health and well-being on individuals than do the rare, major crises (London & Spielberger, 1983). Based on one estimate, 54 percent of all worker absences are in some way stress related, and cost U.S. industries over $150 million per year (Elkin & Rosch, 1990; Karasek & Theorell, 1990). It is the combination of work place environment, personality, gender and job experience that create stressful situations among teachers. It is important to identify the source of stress in agriculture teacher as little recent research has been done in this area. Researching the source of job stress relative to agriculture teachers has implications for improving the nature of the job and may provide insight into possible interventions in cases where stress exist. Purpose and Research Objectives The purpose of the study was to explain and predict job stress among agriculture teachers in Missouri using selected characteristics. The following research objectives were addressed in the study: 1. Describe selected characteristics of secondary agriculture teachers (Gender, Hours a Week at Work, Personality Type, Number of Teachers in Department, Sources of Social Support, Number of Years Teaching, Number of Children, and Number of Years at Current School). 2. Describe the level of job stress among secondary agriculture teachers. 3. Predict job stress from selected characteristics of secondary agriculture teachers. Procedures The design for this study was descriptive-correlational research. The target population was Missouri secondary agriculture teachers (N = 445) during the 2007-2008 academic year. The frame was obtained from the Missouri Department of Elementary and Secondary Education Directory of Agricultural Education. Physical and email addresses for the agriculture teachers were also obtained from the directory. Deliberate effort was made remove duplicate names and ensure an accurate frame was obtained. For this study, a census was sought. Instrumentation Data were collected using the Job Stress Survey (JSS) developed by Spielberger and Vagg (1991). The JSS is a standardized, commercial instrument designed to measure job stress as a function of job-related items that are perceived to be a source of severe and frequent stress. The JSS contained three sections. Section one sought to determine secondary teachers’ perceived level of severity of 30 common job-related stressors using a scale from 1-9; nine being the most stressful measure. The second section sought to determine the frequency secondary teachers encountered the job-related stressor at work during the previous six months using a scale that ranged from zero days experienced to more than nine occurrences in the last six months (0 – 9+). The two responses (severity and frequency) were used to produce three stress index scores: Job Stress 206

Index (JS-X), Lack of Support Index (LS-X), and Job Pressure Index (JP-X). Index scores were calculated by multiplying severity scores by frequency scores. In addition to the three index scores, six subscales were produced to measure various forms of stress. They included: Job Stress-Frequency (JS-F), Job Stress-Severity (JS-S), Lack of Support-Frequency (LS-F), Lack of Support-Severity (LS-S), Job Pressure-Severity (JP-S) and Job Pressure-Frequency (JP-F). A third section was added to the questionnaire which sought teachers’ personal, home and workrelated information. Spielberger and Vagg (1999) report that validity and reliability of the JSS were established through the results of previous studies. The creation of the instrument was detailed in the Job stress survey: Professional Manual. The manual further reported that the job-related items in the JSS were analyzed for construct validity using factor analysis. Alpha coefficients of .89 or higher for the JS-X, JS-S, and JS-F, and .80 or higher for the 10-item JP and LS subscales were reported (Spielberger & Vagg, 1999). Data Collection Data were collected during the months of March and April of 2008. For secondary teachers in Missouri, this period of time can be characterized as representing a high level of activity to included FFA Career Development Event activities as well as typical spring academic semester, instructional activities and events. Three points of contact with secondary teachers were made to collect data. The data collection process began by sending teachers a signed 3”x5” pre-notice postcard announcing the intent of the study and the forth coming email. Two days later a personalized email using the HostedSurvey.com service which included the personalized URL hyperlink to an online questionnaire was sent to subjects. The beginning page of the online instrument contained an opening page message to the teachers detailing the importance of the study and their participation; as well as instructions for completing the online questionnaire. An email reminder was sent via HostedSurvey.com to those who had not responded by the specified date. The email, including the URL (again), further encouraged teacher participation. As a result, a response rate was 42% (n = 193) was achieved. Teachers who responded were assumed to represent response bias. Miller and Smith (1983) suggested procedures for examining response bias by comparing a sampling of non-respondent data to respondent data. Toward that end, two weeks after the first reminder email, a random sample representing 30% (n = 71) of the non-respondents was taken. The sample size was determined following the suggestion of Miller and Smith (1983). Non-respondents were sent a mailed envelope packet containing a revised and signed cover letter, a paper copy of the questionnaire, and a self-addressed, stamped return envelope as a reminder to participate in the study. The final contact with non-respondents was approximately 20 days after the initial mailing, and consisted of a personalized email with a personalized link to the online questionnaire. These efforts yielded an 83% (n = 59) response rate. Data from respondents (n = 193) and non-respondents (n = 59) were statistically compared on the primary variables of interest (J S-X, LS-X, and JP-X). Using an independent samples ttest, no significant (p